bluuebunny/crossref_metadata_2025 huggingface dataset:
Generalizable Clinical Note Section Identification with Large Language Models
Authors: Zhou Weipeng , Miller Timothy A.
Published: December 2024

Objective

Clinical note section identification helps locate relevant information and could be beneficial for downstream tasks such as named entity recognition. But the traditional supervised methods suffer from transferability issues. This study proposes a new framework for using large language models for section identification to overcome the limitations.

Materials and methods

We framed section identification as question-answering and provided the section definitions in free-text. We evaluated multiple LLMs off-the-shelf without any training. We also fine- tune our LLMs to investigate how the size and the specificity of the fine-tuning dataset impacts model performance.

Results

GPT4 achieved the highest F1 score of 0.77. The best open-source model (Tulu2-70b) achieved 0.64 and is on par with GPT3.5 (ChatGPT). GPT4 is also found to obtain F1 scores greater than 0.9 for 9 out of the 27 (33%) section types and greater than 0.8 for 15 out of 27 (56%) section types. For our fine-tuned models, we found they plateaued with an increasing size of the general domain dataset. We also found that adding a reasonable amount of section identification examples is beneficial.

Discussion

These results indicate that GPT4 is nearly production-ready for section identification, and seemingly contains both knowledge of note structure and the ability to follow complex instructions, and the best current open-source LLM is catching up.

Conclusion

Our study shows that LLMs are promising for generalizable clinical note section identification. They have the potential to be further improved by adding section identification examples to the fine-tuning dataset.
The burden of patient healthcare activity in the first year of peritoneal dialysis
Authors: Crowe Kirsty , Murray Eleanor C , MacLeod Joan , Traynor Jamie P , Thomson Peter C
Published: December 2024

Background

The healthcare-related burden associated with kidney replacement therapy (KRT) has not been characterised as comprehensively in peritoneal dialysis (PD) as in haemodialysis (HD) or kidney transplantation. This study aimed to capture the nature and extent of healthcare activity in the first year of PD therapy.

Methods

Retrospective analysis was undertaken on consecutive incident adult patients on PD between 1stJanuary 2015-31stDecember 2019 in the Glasgow Renal and Transplant Unit. Healthcare-related activity was captured up to the first 365 days post-commencement of PD. Data was collected on renal service contact and activity relating to dialysis access, radiological investigation, and relevant infection episodes. Carbon mapping of healthcare activity was estimated using postcode data and previously published carbon footprint estimations.

Results

PD was initiated in 122 patients over the study period. Sixty-three patients (52%) transitioned to another KRT within 365 days of commencing PD. Patients had a mean 36.4 days (SD 22.7) of face-to-face contact days with renal services. This included a mean of 1.5 (SD 1.6) hospital admissions, with a median 5 (IQR 10.8) in-patient days. The estimated carbon footprint was 581kg CO2e/patient over the incident year. This included a median 207kg CO2e/patient for inpatient days and 26kg CO2e/patient for treatment of infections.

Conclusions

There is a significant burden of kidney-associated healthcare on patients commencing their first year of PD despite it being a home-based therapy. Estimates of carbon footprint indicate hotspots include patient travel and hospital admissions, and episodes of peritonitis; a full life cycle analysis is merited.

Key Learning Points

What was known

The characterisation of healthcare-related activity to be expected with peritoneal dialysis therapy is not as comprehensive as that for haemodialysis and kidney transplantation.

Patient-centred realistic medicine requires knowledge of the patient journey and the cumulative impact of healthcare activity interactions.

Healthcare activity burden mapping is required to determine the necessary carbon emission reductions for reducing the contribution to climate change.

This study adds

An illustration of the associated health-care activity burden on patients commencing peritoneal dialysis.

An indication of the carbon footprint associated with peritoneal dialysis health-care activity in the first year of therapy.

Potential impact

Informative for service providers of peritoneal dialysis in anticipating care requirements and planning carbon reduction strategies.

Data can help inform patient decision making when receiving education of kidney replacement therapies.
AGO2 protein: A Key Enzyme in the miRNA Pathway as a Diagnostic and Prognostic Biomarker in Adrenocortical Carcinoma
Authors: Hashmi Anila , Hutvagner Gyorgy , Sidhu Stan , Papachristos Alexander
Published: December 2024

Context

Adrenocortical carcinoma (ACC) is a rare and aggressive malignancy. Current treatment algorithms are associated with diagnostic limitations, high recurrence rates and poor prognosis. Identifying specific biomarkers that facilitate accurate diagnosis and provide prognostic insights could significantly enhance the patient outcomes in ACC.

Objective

To investigate whether microRNA machinery, specifically argonaute 2 (AGO2), a key enzyme in the miRNA pathway, has the potential to be a diagnostic and prognostic biomarker for adrenocortical carcinoma (ACC).

Design

This study analyzed mRNA expression of genes involved in the miRNA biogenesis pathway using RNASeq data from The Cancer Genome Atlas (TCGA) and The Genotype-Tissue Expression (GTEx) dataset, followed by target protein quantification in tissue samples using commercial ELISA kits.

Setting

Publicly available mRNASeq datasets (TCGA-GTEX) and frozen tissue samples from the tumour bank of the Kolling Institute of Medical Research.

Participants

We analyzed data for 79 ACC and 190 normal adrenal cortex (NAC) samples from the TCGA and GTEx datasets, as well as for 31 other cancer types from the TCGA. We then performed protein quantification in 15 NAC, 15 benign adrenal adenoma (AA), and 15 ACC tissue homogenates.

Intervention(s)

None.

Main Outcome Measures

AGO2 mRNA and protein expression in ACC and its prognostic correlation.

Results

AGO2 was significantly overexpressed in ACC, compared to NAC and AA (p<0.001). Kaplan– Meier survival analysis revealed that higher expression of AGO2 was associated with significantly worse overall survival in ACC (HR 7.07, p<0.001). Among all 32 cancer types in TCGA, AGO2’s prognostic utility was most significant in ACC.

Conclusions

AGO2 holds potential as a diagnostic and distinct prognostic biomarker in ACC.
Electrode positioning errors reduce current dose for focal tDCS set-ups: Evidence from individualized electric field mapping
Authors: Niemann Filip , Riemann Steffen , Hubert Ann-Kathrin , Antonenko Daria , Thielscher Axel , Martin Andrew K. , Unger Nina , Flöel Agnes , Meinzer Marcus
Published: December 2024

Objective

Electrode positioning errors contribute to variability of transcranial direct current stimulation (tDCS) effects. We investigated the impact of electrode positioning errors on current flow for tDCS set-ups with different focality.

Methods

Deviations from planned electrode positions were determined using data acquired in an experimental study (N=240 datasets) that administered conventional and focal tDCS during magnetic resonance imaging (MRI). Comparison of individualized electric field modeling for planned and empirically derived “actual” electrode positions was conducted to quantify the impact of positioning errors on the electric field dose in target regions for tDCS.

Results

Planned electrode positions resulted in higher current dose in the target regions for focal compared to conventional montages (7-12%). Deviations from planned positions significantly reduced current flow in the target regions, selectively for focal set-ups (26-30%). Dose reductions were significantly larger for focal compared to conventional set-ups (29-43%).

Conclusions

Precise positioning is crucial when using focal tDCS set-ups to avoid significant reductions of current dose in the intended target regions.

Significance

Our results highlight the urgent need to routinely implement methods for improving electrode positioning, minimization of electrode drift, verification of electrode positions before and/or after tDCS and also to consider positioning errors when investigating dose-response relationships, especially for focal set-ups.
Long-read sequencing of an advanced cancer cohort resolves rearrangements, unravels haplotypes, and reveals methylation landscapes
Authors: O’Neill Kieran , Pleasance Erin , Fan Jeremy , Akbari Vahid , Chang Glenn , Dixon Katherine , Csizmok Veronika , MacLennan Signe , Porter Vanessa , Galbraith Andrew , Grisdale Cameron J. , Culibrk Luka , Dupuis John H. , Corbett Richard , Hopkins James , Bowlby Reanne , Pandoh Pawan , Smailus Duane E. , Cheng Dean , Wong Tina , Frey Connor , Shen Yaoqing , Paulin Luis F. , Sedlazeck Fritz J. , Nelson Jessica M.T. , Chuah Eric , Mungall Karen L. , Moore Richard A. , Coope Robin , Mungall Andrew J. , McConechy Melissa K. , Williamson Laura M. , Schrader Kasmintan A. , Yip Stephen , Marra Marco A. , Laskin Janessa , Jones Steven J.M.
Published: December 2024

The Long-read POG dataset comprises a cohort of 189 patient tumours and 41 matched normal samples sequenced using the Oxford Nanopore Technologies PromethION platform. This dataset from the Personalized Oncogenomics (POG) program and the Marathon of Hope Cancer Centres Network includes accompanying DNA and RNA short-read sequence data, analytics, and clinical information. We show the potential of long-read sequencing for resolving complex cancer-related structural variants, viral integrations, and extrachromosomal circular DNA. Long-range phasing of variants facilitates the discovery of allelically differentially methylated regions (aDMRs) and allele-specific expression, including recurrent aDMRs in the cancer genesRETandCDKN2A. Germline promoter methylation inMLH1can be directly observed in Lynch syndrome. Promoter methylation inBRCA1andRAD51Cis a likely driver behind patterns of homologous recombination deficiency where no driver mutation was found. This dataset demonstrates applications for long-read sequencing in precision medicine, and is available as a resource for developing analytical approaches using this technology.
CHIP disrupts monocyte-endothelial interactions across multiple tissues to promote vascular inflammation in humans
Authors: Parker Alyssa C. , Heimlich J. Brett , Ahmad Ayesha , Bailin Samuel S. , Van Amburg Joseph C. , Vlasschaert Caitlyn , Koethe John R. , Wanjalla Celestine N. , Bick Alexander G.
Published: December 2024

Background

Clonal hematopoiesis of indeterminate potential (CHIP) occurs when hematopoietic stem cells acquire mutations that confer a proliferative advantage. CHIP is associated with increased risk of multiple vascular diseases. Mouse models have identified CHIP monocytes as highly proinflammatory. Here we seek to build on these earlier studies by characterizing adipose tissue macrophages and vascular cells in patients with CHIP.

Methods

We performed single-cell RNA sequencing on paired peripheral blood mononuclear cells and subcutaneous adipose tissue from 6 CHIP patients and 6 matched controls. We analyzed cell type specific gene expression profiles and intercellular interactions.

Results

We found that macrophages had an exaggerated proinflammatory profile compared to circulating monocytes. We also found increased interaction between circulating CD14+ monocytes and endothelial cells. Specifically, cells from patients with CHIP showed enhanced signaling related to leukocyte transendothelial migration. These differences were tissue specific. We recapitulated monocyte-endothelial cell interactions in single cell RNA sequencing data from a mouse model of CHIP.

Conclusions

Monocytes from patients with CHIP have increased endothelial interaction compared to controls and macrophages from patients with CHIP are highly proinflammatory. Alterations in the monocyte-endothelial interaction in CHIP likely contribute to cardiovascular disease risk.

Graphical abstract
Predicting Motor Vigor from EEG Phase Amplitude Coupling (PAC) in Parkinson’s Disease: Effects of Dopaminergic Medication and Non-invasive Modulation
Authors: Kazemi Alireza , Nouri Salar , Mirian Maryam S. , Lee Soojin , McKeown Martin J.
Published: December 2024

Impaired motor vigor is a defining characteristic of Parkinson’s disease (PD), yet the underlying brain mechanisms governing motor vigor (MV) remain unclear. Recent studies have suggested beta-gamma Phase-Amplitude Coupling (PAC) derived from the resting-state electroencephalogram (EEG) is a potential biomarker for PD that is modulated by Deep Brain Stimulation (DBS) and L-dopa treatment. Specifically, PAC has been suggested to be a marker of transitions between motor movements, as opposed to encoding the vigor of the current movement. Here, we comprehensively investigate the potential of various PAC interactions—across different frequency pairs—beyond the linear approaches typically employed to predict MV during motor tasks in PD and study the effects of dopaminergic medication and non-invasive Galvanic Vestibular Stimulation (GVS). We recorded EEG data from 20 PD patients and 22 healthy controls executing a simple, overlearned handgrip task. Subjects were tested on and off L-dopa medication and with and without GVS (multi-sine either 50-100Hz, 100-150Hz). In a preliminary linear (LASSO-based) analysis comparing various PACs and a broad range of commonly used EEG features, PAC features were found to be crucial for predicting MV approximately equally in PD and HC. Initial findings from the linear analysis showed PAC as a significant indicator for MV in both groups, although with variability in cross-validation that implied a complex, non-linear relationship between PAC and MV. To extensively investigate the PAC-MV relation, we used a deep convolutional neural network (PACNET)—developed based on pre-trained VGG-16 architecture—to estimate MV from PAC values. In both PD and HCs, delta-beta, theta-, alpha-, and beta-gamma PACs were important for MV prediction. In PD subjects, GVS affected delta-beta, theta-gamma-, and beta-gamma PACs role in MV prediction, which was sensitive to different GVS stimulation parameters. These PACs were also relevant for PD patients’ MV prediction after L-dopa medication. This study supports the hypothesis that EEG PAC across multiple frequency pairs, not just beta-gamma, predicts MV and not just motor transitions and can be a biomarker for assessing the impact of electrical stimulation and dopaminergic medication in PD. Our results suggest that PAC is involved in MV, in addition to a range of previously reported cognitive processes, including working and long-term memory, attention, language, and fluid intelligence. Non-linear approaches appear important for examining EEG PAC and behavior relations.
A multi-center investigation on the effect of decompressive surgery on Balance and physical ActiviTy Levels in patients with lumbar Spinal stenosis (B-ATLAS): Protocol for a prospective cohort study
Authors: Zielinski OB. , Hallager DW. , Jensen KY. , Carreon LY. , Andersen M.Ø. , Diederichsen LP. , Bech RD.
Published: December 2024

Background

Patients with lumbar spinal stenosis may complain of poor balance, decreased physical function and problems maintaining physical activity levels due to radiculopathy. Decompressive surgery is often indicated if conservative management fails to achieve a satisfactory clinical outcome. While surgical management has proven effective at treating radiculopathy, and patients report increased physical function post-operatively, objective measures of postural control and physical activity remains sparse. This study aims to investigate the effects of decompressive surgery on balance and activity levels of elderly patients with lumbar spinal stenosis using objective measurements.

Methods and analysis

This is a 24-month, multi-centre, prospective cohort study. Patients ≥ 65 years of age with MRI-verified symptomatic lumbar spinal stenosis will be recruited from two separate inclusion centres, and all participants will undergo decompressive surgery for lumbar spinal stenosis. Pre-operative data is collected up to 3 months before surgery, with follow-up data collected at 3, 6, 12 and 24-months post-operatively. Balance measurements are performed using the Wii Balance Board, mini–Balance Evaluation Systems Test and Tandem test, and data concerning physical activity levels are collected using ActiGraph wGT3X-BT accelerometers. Patient reported outcomes regarding quality of life and physical function are collected from the EuroQol-5D, 36-Item Short Form Health Survey and Zurich Claudication Questionnaire. Primary outcomes are the change in sway area of centre of pressure and total activity counts per day from baseline to follow-up at 24-months. A sample size of 80 participants has been calculated.

Ethics and dissemination

The study has been approved by the Regional Ethics Committee of Region Zealand (ID EMN-2022-08110) and the Danish Data Protection Agency (ID REG-100-2022). All results from the study will be published in international peer-reviewed journals and presented at national and international conferences. Study findings will be disseminated through national patient associations.

Trial registration number

NCT06075862 &NCT06057428

STRENGTHS AND LIMITATIONS

This study will provide new knowledge concerning the effects of lumbar spinal decompression surgery on postural control and physical activity levels.

A follow-up period of two years ensures that changes in the primary outcomes are sufficiently documented.

This will be the first study to correlate changes in postural control with changes in physical activity levels amongst patients with lumbar spinal stenosis.

A pre-post prospective cohort design provides the necessary comparative control group.
Pulmonary Hypertension and the Risk of 30-day Postoperative Pulmonary Complications after Gastrointestinal Surgical or Endoscopic Procedures: A Retrospective Propensity Score Weighted Cohort Analysis
Authors: Tatsuoka Yoshio , Carr Zyad J. , Jayakumar Sachidhanand , Lin Hung-Mo , He Zili , Farroukh Adham , Heerdt Paul
Published: December 2024

Background

Pulmonary hypertension (PH) patients are at higher risk of postoperative complications. We analyzed the association of PH with 30-day postoperative pulmonary complications (PPC).

Methods

A single-center propensity score overlap weighting (OW) retrospective cohort study of 164 patients with mean pulmonary artery pressure (mPAP) of >20 mmHg within 24 months of procedure and a control cohort (N=1981), undergoing elective inpatient abdominal surgery or endoscopic procedures under general anesthesia. The primary outcome was PPC, and the secondary outcomes were PPC sub-composites; respiratory failure (RF), pneumonia (PNA), aspiration pneumonia/pneumonitis (ASP), pulmonary embolism (PE), length of stay (LOS), and 30-day mortality.

Results

PPC was higher in the PH cohort (29.9% vs. 11.2%, p<0.001). When sub-composites were analyzed, higher rates of RF (19.3% vs. 6.6%, p<0.001) and PNA (11.2% vs. 5.7%, p=0.01) were observed. After OW, PH was still associated with higher PPC [RR 1.66, 95% CI (1.05 – 2.71), p=0.036] and increased LOS (median 8.0 days vs. 4.9 days) but not 30-day mortality. Sub-cohort analysis showed no difference in PPC between pre- and post-capillary PH patients.

Conclusions

After covariate balancing, PH was associated with a higher risk for PPC and prolonged LOS. This elevated PPC risk should be considered during preoperative risk assessment.
High diagnostic rate of whole genome sequencing in primary ciliary dyskinesia
Authors: Black Holly A , Marion de Proce Sophie , Campos Jose L , Meynert Alison , Halachev Mihail , Marsh Joseph A , Hirst Robert A , O’Callaghan Chris , Genomes Partnership Scottish , Santoyo-Lopez Javier , Murray Jennie , Macleod Kenneth , Urquhart Don S , Unger Stefan , Aitman Timothy J , Mill Pleasantine
Published: December 2024

Aim

Primary ciliary dyskinesia (PCD) is a genetic disorder affecting motile cilia. Most cases are inherited recessively, due to variants in more than 50 genes that result in abnormal or absent motile cilia. This leads to chronic upper and lower airway disease, sub-fertility and laterality defects in some cases. Given overlapping clinical features and genetic heterogeneity, diagnosis can be difficult and often occurs late. Of those tested, an estimated 30% of genetically screened PCD patients still lack a molecular diagnosis. Here, we aimed to identify how readily a genetic diagnosis could be made in a clinically diagnosed population using whole genome sequencing (WGS) to facilitate identification of pathogenic variants in known genes as well as identify novel PCD candidate genes.

Maethods

WGS was used to screen for variants causing PCD in 8 clinically diagnosed PCD patients, sequenced as trios where parental samples were available.

Results

Seven of the eight cases (87.5%) had homozygous or biallelic variants inDNAH5,DNAAF4orDNAH11that were classified as pathogenic or likely pathogenic. Three of the variants were deletions, ranging from 3kb to 13kb, for which WGS identified precise breakpoints, permitting confirmation by Sanger sequencing. WGS yielded a high genetic diagnostic rate from this clinically diagnosed population, in part through detection of structural variants as well as identification of ade novovariant in a novel PCD geneTUBB4B.

Conclusion

A molecular diagnosis allows for appropriate clinical management for cases and their families, including prediction of phenotypic features correlated to genotype. Here, WGS uplifted genetic diagnosis in cases of clinically diagnosed PCD by identifying structural variants and novel modes of inheritance in new candidate genes. Our study suggests that WGS could be a powerful part of the PCD diagnostic toolkit to increase the current molecular diagnostic yield from 70%. It provides important new insight into our understanding of fundamental biology of motile cilia as well as of variation in the non-coding genome in PCD.

Whole genome sequencing (WGS) yielded a high genetic diagnostic rate (100%) in eight Scottish patients with clinically diagnosed primary ciliary dyskinesia (PCD) by detection of large structural variants, homology modelling and identification of a novel disease gene with a dominant mode of inheritance. Prioritised WGS may facilitate early genetic diagnosis in PCD.
Discrimination of Primary and Chronic CMV Infection based on Humoral Immune Profiles in Pregnancy
Authors: Hederman Andrew P. , Remmel Christopher J. , Sharma Shilpee , Natarajan Harini , Weiner Joshua A. , Wrapp Daniel , Donner Catherine , Delforge Marie-Luce , d’Angelo Piera , Furione Milena , Fornara Chiara , McLellan Jason S. , Lilleri Daniele , Marchant Arnaud , Ackerman Margaret E.
Published: December 2024

Most humans have been infected by Cytomegalovirus (CMV) by the time they reach forty years of age. Whereas most of these CMV infections are well controlled by the immune system, congenital infection can lead to serious health effects and death for the fetus and neonate. With clear evidence that risk to the fetus is lower during chronic maternal infection, and varies in association with gestational age at the time of primary maternal infection, further research on humoral immune responses to primary CMV infection during pregnancy is needed. Here, systems serology tools were applied to characterize antibody responses to CMV infection among pregnant and non-pregnant women experiencing either primary or chronic infection. Whereas strikingly different antibody profiles were observed depending on infection status, more limited differences were associated with pregnancy status. Beyond known differences in IgM responses that are used clinically for identification of primary infection, distinctions observed in IgA and FcγR binding antibody responses and among viral antigen specificities accurately predicted infection status in a cross-sectional cohort. Leveraging machine learning, longitudinal samples were also used to define an immunological clock of CMV infection from primary to chronic states and predict time since primary infection with high accuracy. Humoral responses diverged over time in an antigen-specific manner, with IgG3 responses toward tegument decreasing over time as is typical of viral infections, while those directed to pentamer and glycoprotein B were lower during acute and greatest during chronic infection. In sum, this work provides new insights into the antibody response associated with CMV infection status in the context of pregnancy, revealing aspects of humoral immunity that have the potential to improve CMV diagnostics and to support clinical trials of interventions to reduce mother-to-fetus transmission of CMV.
Determining Line of Therapy from Real-World Data in Non-Small Cell Lung Cancer
Authors: Grady Connor B. , Hwang Wei-Ting , Reuss Joshua E. , Iams Wade , Cass Amanda , Liu Geoffrey , Patel Devalben , Liu Stephen V. , Bravo Montenegro Gabriela Liliana , Patil Tejas , Nieva Jorge J. , Herrmann Amanda , Marrone Kristen A. , Lam Vincent K. , Schwartzman William , Dowell Jonathan , Villaruz Liza C , Miller Kelsey Leigh , Weiss Jared , Sun Fangdi , Velcheti Vamsidhar , Camidge D. Ross , Aggarwal Charu , Sun Lova , Marmarelis Melina E.
Published: December 2024

Introduction

Determining lines of therapy (LOT) using real-world data is crucial to inform clinical decisions and support clinical research. Existing rules for determining LOT in patients with metastatic non-small cell lung cancer (mNSCLC) do not incorporate the growing number of targeted therapies used in treatment today. Therefore, we propose rules for determining LOT from real-world data of patients with mNSCLC treated with targeted therapies.

Methods

LOT rules were developed through expert consensus using a real-world cohort of 550 patients withALK+ orROS1+ mNSCLC in the multi-institutional, electronic medical record-based Academic Thoracic Oncology Medical Investigators Consortium’s (ATOMIC) Driver Mutation Registry. Rules were subsequently modified based on a review of appropriate LOT determination. These resulting rules were then applied to an independent cohort of patients withEGFR+ mNSCLC to illustrate their use.

Results

Six rules for determining LOTs were developed. Among 1133 patients withEGFRmutations and mNSCLC, a total of 3168 regimens were recorded with a median of 2 regimens per patient (IQR, 1-4; range, 1-13). After applying our rules, there were 2834 total LOTs with a median of 2 LOTs per patient (IQR, 1-3; range, 1-11). Rules 1-3 kept 11% of regimen changes from advancing the LOT. When compared to previously published rules, LOT assignments differed 5.7% of the time, mostly in LOTs with targeted therapy.

Conclusion

These rules provide an updated framework to evaluate current treatment patterns, accounting for the increased use of targeted therapies in patients with mNSCLC and promote standardization of methods for determining LOT from real-world data.

Key Points

Use of targeted therapy to treat patients with mNSCLC is growing

Determining lines of therapy from real-world data is crucial for clinical research

Our rules aim to advance the line of therapy to reflect changes in clinical status

Using these rules can lead to better method harmonization in mNSCLC research
Variation in health visiting for the under 5s: A cross-sectional analysis of administrative data in England for 2018-2020
Authors: Liu Mengyun , Woodman Jenny , Grath-Lone Louise Mc , Clery Amanda , Bunting Catherine , Bennett Samantha , Kendall Sally , Kirman Jennifer , Weatherly Helen , Barlow Jane , Bedford Helen , Harron Katie
Published: December 2024

Introduction

The health visiting service in England leads the delivery of the government’s Healthy Child Programme (HCP) for children under five. However, local authorities and their provider partners deliver this service differently in respond to the challenges of funding cuts, low numbers of health visitors and high levels of family needs.

Objective

We aimed to describe local authority variation in the delivery of health visiting services to children under 5 in England between 2018 and 2020.

Methods

We used nationally published statistics on mandated health visiting contacts, and administrative data from the Community Services Dataset (CSDS) on duration, location, and medium of delivery of contacts. We mapped the population coverage of mandated contacts (new birth visit, 6-8-week review, one-year review and 2-2 ½year review), and additional contacts, and described the frequency and characteristics of contacts across local authorities.

Results

According to the published statistics, 99% of eligible children received their new birth visit, 89% received the 6-8-week review and the one-year review, and 82% received the 2-2½-year review. There was substantial variation across local authorities: coverage for the 2-2½-year review ranged between 33%-97%. Based on CSDS, 80% of local authorities (n=46/57) delivered more additional contacts than mandated contacts: on average, 1.6 additional contacts (range: 0.1-8.5) were delivered for each mandated contact. There was also significant variation in the duration of contacts and the percentage of contacts delivered face-to-face and at home.

Conclusions

Our study demonstrates substantial variation in the delivery of health visiting services across England, particularly in the delivery of additional contacts. Further research is needed to explore the extent to which this trend has continued and the reasons for this variation. There is also a need to exploit this data to further explore the impact of these different models of service delivery on family outcomes.
Advancing primary care for childhood pneumonia: a machine learning-based approach to prognosis and case management
Authors: Serin Oguzhan , Akbasli Izzet Turkalp , Bocutcu Cetin Sena , Koseoglu Busra , Deveci Ahmet Fatih , Ugur Muhsin Zahid , Ozsurekci Yasemin
Published: December 2024

Background

Pneumonia is the leading cause of preventable mortality under five years of age. Appropriate case management is as essential as disease prevention interventions, especially in primary care settings. Computer science has been used accurately and widely for pneumonia diagnosis; however, prognosis studies are relatively low. Herein, we developed a machine learning-based clinical decision support system tool for childhood pneumonia to provide prognostic support for case management.

Methods

We analyzed data from 437 children admitted to our clinic with a pneumonia diagnosis between 2014 and 2020. Pediatricians encoded the raw dataset according to candidate features. Before the experimental study of the machine learning algorithms of Pycaret, SMOTE-Tomek was utilized for managing imbalanced datasets. The feature selection was made by examining the SHAP values of the algorithm with the highest performance and re-modeled with the most important clinical features. We optimized hyperparameters and employed ensemble methods to develop a robust predictive model.

Results

Optimized models predicted pneumonia prognosis with %77-88 accuracy. It was shown that severity could be determined over %84 by five clinical features: hypoxia, respiratory distress, age, Z score of weight for age, and antibiotic usage before admission.

Conclusions

In this experimental study, we demonstrated that contemporary data science methods, such as oversampling, feature selection, and machine learning tools, are promising in predicting the critical care need of patients. Even in small-size samples like our study, ML methods can reach current wisdom.

Highlights

Pneumonia accounts for 14% of mortality in children under 5, with over 740,000 deaths in 2019 alone.

WHO and UNICEF’s GAPPD aims to cut mortality rates by focusing on vaccinations, sanitation, breastfeeding, and addressing pediatric HIV.

Accurate diagnosis and timely treatment can reduce pneumonia mortality by up to 28%, yet diagnosing can be challenging.

Many in underdeveloped regions lack access to essential equipment and trained staff, exacerbating mortality rates.

Data science and machine learning offer promising solutions for pneumonia management, especially in LMICs, with a focus on prognostic support.
seg-metrics: a Python package to compute segmentation metrics
Authors: Jia Jingnan , Staring Marius , Stoel Berend C.
Published: December 2024

Medical image segmentation (MIS) is an important task in medical image processing. Unfortunately, there is not a out-of-the-box python package for the evaluation metrics of MIS. Therefore, we developedseg-metrics, an open-source Python package for MIS model evaluation. Unlike existing packages,seg-metricsoffers user-friendly interfaces for various overlap-based and distance-based metrics, providing a comprehensive solution.seg-metricssupports multiple file formats and is easily installable through the Python Package Index (PyPI). With a focus on speed and convenience,seg-metricsstands as a valuable tool for efficient MIS model assessment.
Divergent treatment responses in chronic pain: Identifying subgroups of patients through cluster analysis
Authors: Rijsdijk Mienke , Smits Hidde M. , Azizoglu Hazal R. , Brugman Sylvia , van de Burgt Yoeri , van Charldorp Tessa C. , van Gelder Dewi J. , de Grauw Janny C. , van Lange Eline A. , Meye Frank J. , Strick Madelijn , Walravens Hedi , Winkens Laura H.H. , Huygen Frank , Drylewicz Julia , Willemen Hanneke L.D.M.
Published: December 2024

Background

Chronic pain is an ill-defined disease with complex biopsychosocial aspects, posing treatment challenges. We hypothesize that treatment failure results, at least partly, from limited understanding of diverse patient subgroups. We aim to identify subgroups through psychometric data, allowing for more tailored interventions.

Methods

For this retrospective cohort study, we extracted patient-reported data from two Dutch tertiary multidisciplinary outpatient pain clinics (2018-2023) for unsupervised hierarchical clustering. Clusters were defined by anxiety, depression, pain catastrophizing, and kinesiophobia. Sociodemographics, pain characteristics, diagnosis, lifestyle, health-related quality of life (HRQoL) and treatment efficacy were compared among clusters. A prediction model was built utilizing a minimum set of questions to reliably assess cluster allocation.

Results

Among 5,454 patients with chronic pain, three clusters emerged. Cluster 1 (n=750) was characterized by high psychological burden, low HRQoL, lower educational levels and employment rates, and more smoking. Cluster 2 (n=1,795) showed low psychological burden, intermediate HRQoL, higher educational levels and employment rates, and more alcohol consumption. Cluster 3 (n=2,909) showed intermediate features. Pain reduction following treatment was least in cluster 1 (28.6% after capsaicin patch, 18.2% after multidisciplinary treatment), compared to >50% in clusters 2 and 3. A model incorporating 15 psychometric questions reliably predicted cluster allocation.

In conclusion,our study identifies distinct chronic pain patient clusters through 15 psychometric questions, revealing one cluster with notably poorer response to conventional treatment. Our prediction model may help clinicians improve treatment by allowing patient-subgroup targeted therapy according to cluster allocation.

In brief

Hierarchical clustering of chronic pain patients revealed three clusters based on pain experience and psychological welfare, with diverse sociodemographics and treatment effects suggesting potential for tailored interventions.
Improving the care of children with GENetic Rare disease: Observational Cohort study (GenROC): a study protocol
Authors: Low KJ , Watford A , Blair PS , Nabney I , Powell J , Wynn SL , Foreman J , Firth HV , Ingram JC.
Published: December 2024

Around 2000 children are born in the UK per year with a neurodevelopmental genetic syndrome with significantly increased morbidity and mortality(1). Often little is known about expected growth and phenotypes in these children. Parents have responded by setting up social media groups to generate data themselves. Given the significant clinical evidence gaps, this research will attempt to identify growth patterns, developmental profiles and phenotypes, providing data on long-term medical and educational outcomes. This will guide clinicians when to investigate, monitor or treat symptoms and when to search for additional or alternative diagnoses.

Methods and analysis

This is an observational, multicentre cohort study recruiting between March 2023 and February 2026. Children aged 6 months up to 16 years with a pathogenic or likely pathogenic variant in a specified gene will be eligible. Children will be identified through the NHS and via self-recruitment. Parents or carers will complete a questionnaire at baseline and again one year after recruitment. The named clinician (in most cases a clinical geneticist) will complete a clinical proforma which will provide data from their most recent clinical assessment. Qualitative interviews will be undertaken with a subset of parents partway through the study. Growth and developmental milestone curves will be generated through the DECIPHER website (https://deciphergenomics.org) where 5 or more children have the same genetic syndrome (at least ten groups expected).

The results will be presented at national and international conferences concerning the care of children with genetic syndromes. Results will also be submitted for peer review and publication.

Article Summary

Strengths and Limitations of this study

This study is a collaborative effort which combines previous data with that of a large cohort to maximise possible outputs through the DECIPHER database.

The study is the first of its kind to acquire natural history clinical data from a large cohort across the UK to ascertain both gene specific data but also cross syndrome cohort metrics.

The study utilises the Musketeers Memorandum to facilitate recruitment from genetics centres across the UK and also empowers families to volunteer directly which will be supported by a national patient support charity.

This study does not recruit individuals over the age of 15 and so longer-term outcomes will not be evaluated. Further studies of older individuals will be required to gather their data and we hope that a follow-on study would be possible to enable this.

Data are largely gathered through online questionnaires completed by both parent and clinician. The quality of data received will vary depending on the user. This method of data collection may result in selection bias against certain demographic groups which will need to be accounted for in any discussion regarding the generalisability of the results.
A Protocol for a Scoping Study of Economic and Data System Considerations for Climate Change and Pandemic Preparedness in Africa
Authors: Brunn Ariel , Ruiz Francis , Falconer Jane , Delight Ellie , Gerard Jessica , Lui Yang , Bett Bernard , Bah Bubacarr , Uzochukwu Benjamin , Murray Kris
Published: December 2024

Background

Africa’s changing climate heightens risk of disease outbreaks with impacts on vulnerable demographics already encumbered by health and socioeconomic burdens. Health preparedness policies to address disease outbreaks rely on integrated information systems and value-for-cost analysis to facilitate sound decision-making during a public health emergency. This rapid scoping review protocol describes thea prioriobjectives and methods to conduct a synthesis of the evidence on economic evaluation and data system capacity at the intersection of pandemic preparedness and climate change.

Methods

A search of six bibliographic databases will be conducted by a library information professional spanning the period 2010 – 2023, focused on literature published about pandemic preparedness in Africa in the context of climate change. Studies will be screened in a three-stage process by independent reviewers using Covidence software, with a proportion of excluded articles crosschecked by a reviewer not involved in screening. All articles included in the final study set will need to have a positive response to at least three out of foura prioriscreening questions. Data extraction will follow established frameworks for pandemic preparedness, a list of 14 target climate-sensitive infectious diseases with pandemic potential declared as Public Health Emergencies of International Concern and listed on the WHO’s R&D Blueprint Pathogens, and economic evaluation or data systems domains. Evidence synthesis will include article bibliometric analysis as well as thematic topic categorisation. Gap analysis will be conducted through topic mapping.

Discussion

This protocol lists the methods and analysis that will be followed to survey the literature on the linkages between climate change and pandemic preparedness relating to economic evaluation and data systems structures and needs.
A systematic scoping review of studies describing human factors, human-centered design, and usability of sensor-based digital health technologies
Authors: Tandon Animesh , Cobb Bryan R , Centra Jacob , Izmailova Elena , Manyakov Nikolay V. , McClenahan Samantha , Patel Smit , Sezgin Emre , Vairavan Srinivasan , Vrijens Bernard , Bakker Jessie P
Published: December 2024

Background

Increasing adoption of sensor-based digital health technologies (sDHTs) in recent years has cast light on the many challenges in implementing these tools into clinical trials and patient care at scale across diverse patient populations; however, the methodological approaches taken towards sDHT usability evaluation have varied markedly.

Objective

To elucidate the current landscape of studies reporting data related to sDHT human factors, human-centered design, and/or usability.

Methods

We conducted a systematic scoping review of studies published between 2013 and 2023 and indexed in PubMed, in which data related to sDHT human factors, human-centered design, and/or usability were reported. Following a systematic screening process, we extracted the study design; participant sample; the sDHT(s) used; the method(s) of data capture; and the type(s) of usability-related data captured.

Results

Our literature search returned 442 papers, of which 85 were found to be eligible and 83 were available for data extraction and not under embargo. In total, 164 sDHTs were evaluated; 141 were wearable tools while the remaining 23 were ambient tools. The majority of studies (n=55; 66%) reported summative evaluations of final-design sDHTs. Almost all studies (n=82; 98%) captured data from targeted end-users, but only 18 (22%) captured data from additional users such as carepartners or clinicians. User satisfaction and ease of use were evaluated for >80% of sDHTs; however, learnability, eficiency, and memorability were reported for only 11 (13%), 4 (5%), and 2 sDHTs (2%), respectively. Fourteen sDHTs (17%) were evaluated according to the extent to which users were able to understand the clinical data or other information presented to them (understandability) and/or the actions or tasks they should complete in response (actionability). Notable gaps in reporting included the absence of a sample size rationale (reported for 25% of all studies and 31% of summative studies) and incomplete sociodemographic descriptive data (complete age, sex/gender, and race/ethnicity reported for 17% of studies).

Conclusions

Based on our findings, we suggest four actionable recommendations for future studies that will help to advance the implementation of sDHTs: 1) Consider in-depth assessment of technology usability beyond user satisfaction and ease of use; 2) Expand recruitment to include important user groups such as clinicians and carepartners; 3) Report the rationale for key study design considerations including the sample size; and 4) Provide rich descriptive statistics regarding the study sample to allow a complete understanding of generalizability to other patient populations and contexts of use.
Development and exploratory analysis of a multi-dimensional metric of adherence for digital health interventions
Authors: Mason Harry T. , O’Connor Siobhán , Wong David , Stanmore Emma
Published: December 2024

Introduction

Adherence is often cited as an important metric to demonstrate sustained engagement of an individual or population with a health technology, but its definition is often ill-defined. Any adherence definition made for digital health interventions must be clearly defined to ensure a consistent approach to measuring sustained use as an indicator of impact.

Methods

This study followed mathematically-defined definitions of distinct aspects of adherence: initial adoption, consistency, duration, and dropout. These were then applied to a digital physiotherapy dataset of older adults (N=56). Participants were assigned 3 sessions a week of exergames (exercise-based videogames) for 12 weeks using MIRA rehab software platform.

Results

The following adherence characteristics emerged: an initial dropout of 3% (completed ≤3 sessions), 20% of participants achieving the desired consistency (≥3 sessions a week for 12 weeks), 39% of participants passing a duration threshold (completing ≥20 minutes a week for 12 weeks), and an average dropout at 72.3% (when judged by percentage of sessions completed at dropout).

Conclusion

The approach used for measuring and reporting adherence metrics allows readers to draw clear conclusions about the different aspects of engagement that users displayed with the digital health programme. This type of reporting is recommended for all future digital health studies reporting adherence measures to ensure a consistent approach to reporting and comparing digital health interventions and their impact.
Global patterns of rebound to normal RSV dynamics following COVID-19 suppression
Authors: Thindwa Deus , Li Ke , Cooper-Wootton Dominic , Zheng Zhe , Pitzer Virginia E , Weinberger Daniel M
Published: December 2024

Introduction

Annual epidemics of respiratory synctial virus (RSV) had consistent timing and intensity between seasons prior to the SARS-CoV-2 pandemic (COVID-19). However, starting in April 2020, RSV seasonal activity declined due to COVID-19 non-pharmaceutical interventions (NPIs) before re-emerging after relaxation of NPIs. We described the unusual patterns of RSV epidemics that occurred in multiple subsequent waves following COVID-19 in different countries and explored factors associated with these patterns.

Methods

Weekly cases of RSV from twenty-eight countries were obtained from the World Health Organisation and combined with data on country-level characteristics and the stringency of the COVID-19 response. Dynamic time warping and regression were used to describe epidemic characteristics, cluster time series patterns, and identify related factors.

Results

While the first wave of RSV epidemics following pandemic suppression exhibited unusual patterns, the second and third waves more closely resembled typical RSV patterns in many countries. Post-pandemic RSV patterns differed in their intensity and/or timing, with several broad patterns across the countries. The onset and peak timings of the first and second waves of RSV epidemics following COVID-19 suppression were earlier in the Southern Hemisphere. The second wave of RSV epidemics was also earlier with higher population density, and delayed if the intensity of the first wave was higher. More stringent NPIs were associated with lower RSV growth rate and intensity and a shorter gap between the first and second waves.

Conclusion

Patterns of RSV activity have largely returned to normal following successive waves in the post-pandemic era. Onset and peak timings of future epidemics following disruption of normal RSV dynamics need close monitoring to inform the delivery of preventive and control measures.