bluuebunny/crossref_metadata_2025_split huggingface dataset:
Environmental and social impact assessment in healthcare business planning: a national survey of NHS trusts in England
Authors: Takahashi Haruna , Kudsk-Iversen Søren 
Published: November 2024

AbstractBackgroundDecision-making in healthcare should include considerations for its environmental and social consequences as well as clinical and financial. Business cases represent projects which are likely to have particularly high impact, in which performing an Environmental and Social Impact Assessment may reduce negative effects. The practice of assessing these impacts is not mandatory, therefore our aim was to determine to what extent environmental and social considerations are incorporated into business planning across NHS trusts in England. We also set out to identify examples of good practice, common challenges and possible solutions to overcome these.MethodsWe conducted a cross-sectional survey across trusts in NHS England to capture current practice. This was carried out online between April and July 2024.ResultsNinety seven out of 210 trusts (46%) responded to the survey. Thirty two currently assess the environmental and/or social impact of their business cases and 25 are in the process of doing so, illustrating a growing practice. There is significant variation between trusts in terms of content, format and process of the assessment.DiscussionThis was the first study to portray not only whether and how environmental and social impacts are assessed in NHS business planning, but also the barriers and potential solutions in embedding this assessment. The variation in practice signifies heterogeneity of NHS trusts and there is unlikely to be a single tool that would suit every organisation. An impact assessment process with a clear purpose, that reflects organisational values, and is supported by multidisciplinary expertise is likely to be more meaningful. We include an Environmental and Social Impact Assessment tool for our trust, which has been developed based on the learning points from this study and modified to our trust’s needs, as an example.
Explainable machine learning to identify patients at risk of developing hospital acquired infections
Authors: Creagh Andrew P. , Pease Tom , Ashworth Philip , Bradley Lloyd , Duport Sophie 
Published: November 2024

AbstractHospital-acquired infections (HAIs) contribute to increased mortality rates and extended hospital stays. Patients with complex neurological impairments, secondary to conditions such as acquired brain injury or progressive degenerative conditions are particularly prone to HAIs and often have the worst resulting clinical outcomes and highest associated cost of care. Research indicates that the prompt identification of such infections can significantly mitigate mortality rates and reduce hospitalisation duration. The current standard of care for timely detection of HAIs for inpatient acute and post-acute care settings in the UK is the National Early Warning Score v02 (NEWS2). NEWS2, despite its strengths, has been shown to have poor prognostic accuracy for specific indications, such as infections. This study developed a machine learning (ML) based risk stratification tool, utilising routinely collected patient electronic health record (EHR) data, encompassing over 800+ patients and 400k+ observations collected across 4-years, aimed at predicting the likelihood of infection in patients within an inpatient care setting for patients with complex acquired neurological conditions. Built with a combination of historical patient data, clinical coding, observations, clinician reported outcomes, and textual data, we evaluated our framework to identify individuals with an elevated risk of infection within a 7-day time-frame, retrospectively over a 1-year “silent-mode” evaluation. We investigated several time-to-event model configurations, including manual feature-based and data-driven deep generative techniques, to jointly estimate the timing and risk of infection onset. We observed strong performance of the models developed in this study, achieving high prognostic accuracy and robust calibration from 72–6 hours prior to clinical suspicion of infection, with AUROC values ranging from 0.776–0.889 and well-calibrated risk estimates exhibited across those time intervals (IBS<0.178). Furthermore, by assigning model-generated risk scores into distinct categories (low, moderate, high, severe), we effectively stratified patients with a higher susceptibility to infections from those with lower risk profiles. Post-hoc explainability analysis provided valuable insights into key risk factors, such as vital signs, recent infection history, and patient age, which aligned well with prior clinical knowledge. Our findings highlight our framework’s potential for accurate and explainable insights, facilitating clinician trust and supporting integration into real-world patient care workflows. Given the heterogeneous and complex patient population, and our under-utilisation of the data recorded in routine clinical notes and lab reports, there are considerable opportunities for performance improvement in future research by expanding our model’s multimodal capabilities, generalisability, and additional model personalisation steps.
Feasibility Study on the Application of Transcranial Focused Ultrasound Stimulation in Ischemic Stroke Rehabilitation
Authors: Jung JeYoung , Cho Minjae , Lee Sekwang , Yoo Hyun-Joon , Lambon Ralph Matthew A. , Pyun Sung-Bom 
Published: November 2024

AbstractStroke is a leading cause of long-term neurological disability and death worldwide. Traditional neurorehabilitation methods result in full recovery for less than 15% of stroke patients, highlighting the need for innovative approaches. Low-intensity transcranial focused ultrasound stimulation (tFUS) has emerged as a promising non-invasive neuromodulation technique with high focality and deep tissue penetration. This study evaluates the feasibility, safety and efficacy of tFUS for motor rehabilitation in ischemic stroke patients. We conducted a single-group prospective pilot study involving two stroke patients who received tFUS targeting the motor cortex contralateral to the lesion site three times a week. Patients were evaluated using multiple clinical measures of motor function. Both patients showed improvements in the Fugl-Meyer Assessment upper extremity score, kinesthetic sensation, stereognosis score from the Nottingham Sensory Assessment, and the Modified Barthel Index score. Follow-up evaluations immediately and three weeks after stimulation indicated sustained motor function improvements. The results suggest that tFUS can effectively enhance motor recovery in stroke patients without adverse events. This study provides robust evidence supporting the potential of tFUS in stroke rehabilitation, opening new avenues for clinical application and research.
Clinical Sentiment Analysis by Large Language Models Enhances the Prediction of Hepatorenal Syndrome in Decompensated Cirrhosis
Authors: Lai Mason , Fenton Cynthia , Rubin Jessica , Huang Chiung-Yu , Pletcher Mark , Lai Jennifer C. , Cullaro Giuseppe , Ge Jin 
Published: November 2024

AbstractBackground and AimsHepatorenal syndrome – Acute Kidney Injury (HRS-AKI) is a severe complication of decompensated cirrhosis that is challenging to predict. Sentiment analysis, a computational process of identifying and categorizing opinions and judgment expressed in text, may enhance traditional prediction methodologies based on structured variables. Large language models (LLMs), such as generative pretrained transformers (GPTs), have demonstrated abilities to perform sentiment analyses on non-clinical texts. We sought to determine if GPT-performed sentiment analysis could improve upon predictions using clinical covariates alone in the prediction of HRS-AKI.MethodsAdult patients admitted to a single academic medical center with decompensated cirrhosis and AKI. We used a protected health information (PHI) compliant version of Microsoft Azure OpenAI GPT-4o to derive a sentiment score ranging from 0 to 1 for HRS-AKI, and conduct natural language processing (NLP) extraction of clinical terms associated with HRS-AKI in clinical notes. The area under the receiver operator curve (AUROC) was compared in logistic regression models incorporating structured variables (socio-demographics, MELD 3.0, hemodynamic parameters) with compared to without sentiment scores and NLP-extracted clinical terms.ResultsIn our cohort of 314 participants, higher sentiment score was associated with the diagnosis of HRS-AKI (OR 1.33 per 0.1, 95% CI 1.02-1.79) in multivariate models. AUROC of the baseline model using structured clinical covariates alone was 0.639. With the addition of the GPT-4o derived sentiment score and clinical terms to structured covariates, the final model yielded an improved AUROC of 0.758 (p= 0.03).ConclusionsClinical texts contain large amounts of data that are currently difficult to extract using standard methodologies. Sentiment analysis and NLP-based variable derivation with GPT-4o in clinical application is feasible and can improve the prediction of HRS-AKI over traditional modeling methodologies alone.
Age of menopause and dementia risk in 10,832 women from the Swedish Twin Registry
Authors: Saelzler Ursula G. , Sundermann Erin E. , Foret Janelle T. , Gatz Margaret , Karlsson Ida K. , Panizzon Matthew S. 
Published: November 2024

AbstractINTRODUCTIONAn earlier age of menopause (AOM) is hypothesized to increase vulnerability to the neuropathological processes of dementia which begin in midlife.METHODSWe tested this hypothesis in a sample of 10,832 women from the Swedish Twin Registry, stratified by menopause etiology. Survival models showed that a U-shaped association was present for women whose menopause occurred spontaneously. Sensitivity analyses conducted in hormone naïve,APOEε4+ and AOM restricted subsamples showed largely analogous patterns of results.DISCUSSIONSupporting conclusions from basic research, our results suggest that estrogens (proxied here by AOM) interact with several biological pathways mediating dementia disease processes. In line with trends in hormone research across the past century, our findings challenge the oversimplified ’more-is-better’ perspective on hormone exposure. Specifically, the non-linear association we observed between AOM and dementia risk points to the involvement of distinct and interacting biological mechanisms beyond just estrogen levels.
HELMET: A Hybrid Machine Learning Framework for Real-Time Prediction of Edema Trajectory in Large Middle Cerebral Artery Stroke
Authors: Phillips Ethan , O’Donoghue Odhran , Zhang Yumeng , Tsimpos Panos , Mallinger Leigh Ann , Chatzidakis Stefanos , Pohlmann Jack , Du Yili , Kim Ivy , Song Jonathan , Brush Benjamin , Smirnakis Stelios , Ong Charlene J , Orfanoudaki Agni 
Published: November 2024

AbstractMalignant cerebral edema occurs when brain swelling displaces and compresses vital midline structures within the first week of a large middle cerebral artery stroke. Early interventions such as hyperosmolar therapy or surgical decompression may reverse secondary injury but must be administered judiciously. To optimize treatment and reduce secondary damage, clinicians need strategies to frequently and quantitatively assess the trajectory of edema using updated, relevant information. However, existing risk assessment tools are limited by the absence of structured records capturing the evolution of edema and typically estimate risk at a single time point early in the admission, therefore failing to account for changes in variables over the following hours or days. To address this, we developed and validated dynamic machine learning models capable of accurately predicting the severity of midline structure displacement, an established indicator of malignant edema, in real-time. Our models can provide updated estimations as frequently as every hour, using data from structured time-varying patient records, radiographic text, and human-curated neurological characteristics. Our work resulted in two novel multi-class classification models, collectively named Hybrid Ensemble Learning Models for Edema Trajectory (HELMET), predicting the progression of midline shift over 8-hour (HELMET-8) and 24-hour windows (HELMET-24), respectively. HELMET combines transformer-based large language models with supervised ensemble learning, demonstrating the value of merging human expertise and multimodal health records in developing clinical risk scores. Both models were trained on a retrospective cohort of 15,696 observations from 623 patients hospitalized with large middle cerebral artery ischemic stroke and were externally validated using 3,713 observations from 60 patients at a separate hospital system. Our HELMET models are accurate and generalize effectively to diverse populations, achieving a cross-validated mean area under the receiver operating characteristic score of 96.6% in the derivation cohort and 92.5% in the external validation cohort. Moreover, our approach provides a framework for developing hybrid risk prediction models that integrate both human-extracted and algorithm-derived multi-modal inputs. Our work enables accurate estimation of complex, dynamic, and highly specific clinical targets, such as midline shift, in real-time, even when relevant structured information is limited in electronic health record databases.
Genome-wide association study of varenicline-aided smoking cessation
Authors: Coley Kayesha , Wang Qingning , Packer Richard , John Catherine , Abner Erik , Reis Kadri ,   Estonian Biobank Research Team, Bedair Khaled F. , Srinivasan Sundararajan , Paciga Sara , Hyde Craig , Free Robert C. , Reeve Nicola F. , Shepherd David J. , Esko Tõnu , Palmer Colin , Pearson Ewan , Malarstig Anders , Tobin Martin D. , Batini Chiara 
Published: November 2024

AbstractIntroductionVarenicline is an α4β2nicotinic acetylcholine receptor partial agonist with the highest therapeutic efficacy of any pharmacological smoking cessation aid and a 12-month cessation rate of 26%. Genetic variation may be associated with varenicline response, but to date no genome-wide association studies of varenicline response have been published.MethodsIn this study, we investigated the genetic contribution to varenicline effectiveness using two electronic health record-derived phenotypes. We defined short-term varenicline effectiveness (SVE) and long-term varenicline effectiveness (LVE) by assessing smoking status at 3 and 12 months, respectively, after initiating varenicline treatment. In Stage 1, comprising five European cohort studies, we tested genome-wide associations with SVE (1,405 cases, 2,074 controls) and LVE (1,576 cases, 2,555 controls), defining sentinel variants (the most strongly associated variant within 1 megabase) withp-value <5×10−6to follow up in Stage 2. In Stage 2, we tested association between sentinel variants and comparable smoking cessation endpoints in varenicline randomised controlled trials. We subsequently meta-analysed Stages 1 and 2.ResultsNo variants reached genome-wide significance in the meta-analysis. In Stage 1, 10 sentinel variants were associated with SVE and five with LVE at a suggestive significance threshold (p-value <5×10−6). None of these sentinels were previously implicated in varenicline-aided smoking cessation or in genetic studies of smoking behaviour.ConclusionsWe provide initial insights into the biological underpinnings of varenicline-aided smoking cessation, through implicating genes involved in various processes, including gene expression, cilium assembly and early-stage development.ImplicationsLeveraging electronic health records, we undertook the largest genetic study of varenicline-aided smoking cessation to date, and the only such study to test genome-wide associations. We showed distinct genetic variants associated (p-value <5×10−6) with varenicline-aided smoking cessation which implicate diverse cellular functions, including transcriptional regulation, RNA modification and cilium assembly. These provide insights which, if independently corroborated, will improve understanding of varenicline response. The growing availability of biobank resources with genetic and varenicline response data will provide future opportunities for larger studies using the approach we developed.
Parental and individual socioeconomic position show distinct associations with trajectories of diet quality across adolescence and early adulthood
Authors: Rousham Oscar , Braune Tanya , Mori Trevor A , Beilin Lawrence J , Winpenny Eleanor M 
Published: November 2024

AbstractBackgroundAdolescence and early adulthood is an important period for the development of health behaviours and diet quality, which may persist through adult life. Socioeconomic position (SEP) during early adulthood shows associations with inequalities in diet quality and cardiometabolic health later in adulthood, yet little is known about how dietary inequalities develop during adolescence and early adulthood. This study aimed to identify distinct diet quality trajectories observed across adolescence and early adulthood; and explore the association between parental and individual SEP and these trajectories.MethodsThe Raine Study is a large multigenerational cohort (n=1984) based in Western Australia. Dietary data was self-reported at five time points from ages 14 to 27 years. Growth mixture modelling was used to identify distinct diet quality trajectories based on Dietary Approaches to Stop Hypertension (DASH) diet scores. Multinomial logistic regression was used to determine the associations of parental SEP (maternal education level; and household income at age 14) and individual SEP (school leaving age) with diet quality trajectory membership.ResultsThree diet quality trajectories were identified: consistently low quality (41%), low-to-high quality (42%), and consistently high quality (16%). The ‘low-to-high’ diet quality trajectory showed a steep increase in diet quality from ages 14 to 20 years. Higher parental SEP was associated with a higher diet quality trajectory. After adjusting for parental SEP, leaving school before year 11 remained strongly associated with the low diet quality trajectory.ConclusionOur study demonstrates the development of dietary inequalities across adolescence and early adulthood, highlighting the importance of emerging individual SEP, in addition to parental SEP in shaping this. Interventions to improve diet quality in early adulthood are particularly needed among those who leave education at a young age.
Distinct rates of VUS reclassification are observed when subclassifying VUS by evidence level
Authors: Bennett Gwendolyn , Karbassi Izabela , Chen Wenjie , Harrison Steven M. , Lebo Matthew S. , Meng Linyan , Nagan Narasimhan , Rigobello Robert , Rehm Heidi L. 
Published: November 2024

AbstractPurposeGenetic testing commonly yields a plethora of variants of uncertain significance (VUS) that can lead to ongoing uncertainty for patients and their caregivers. While all VUS hold uncertainty, some VUS have more evidence in support of pathogenicity while others have more evidence of a benign role. Sharing these nuances can help guide the investment in follow-up clinical and research investigations and may, at times, influence medical decision-making despite appreciated uncertainty.MethodsFour clinical laboratories have been subclassifying VUS to help prioritize investigation and guide reporting decisions. Each laboratory developed a distinct approach for how these subclasses are used in their laboratories and, in some cases, displayed on reports. We examined the composition of each laboratory’s VUS subclasses and the likelihood variants from each subclass were reclassified towards pathogenic or benign.ResultsWe found that variants in the lowest subclass of VUS were never reclassified as likely pathogenic (LP) or pathogenic (P), while those in the highest subclass were much more likely to be reclassified as P/LP.ConclusionGiven that forthcoming professional guidance in variant classification will advise the use of VUS subclasses, the experience of our laboratories in using VUS subclasses can inform future practices.
Product Features Used to Promote Top-selling Cannabis Vape Products in An Online Retail Environment
Authors: Chen-Sankey Julia , Capria Kathryn La , Meng Siyan , Mazzeo Rosanna , Vijayakumar Neha , Padon Alisa A. , Wagoner Kimberly G. , Moran Meghan B. , Rossheim Matthew , Berg Carla , Jackson Kristina M. 
Published: November 2024

ABSTRACTIntroductionExposure to commercial marketing of cannabis vape products (CVPs) may encourage CVP use, yet little is known about how these products are marketed. This study examined marketing descriptions of CVPs’ functions and benefits in an online retail environment.MethodsProduct descriptions from a sample of 343 CVPs from 78 top-selling cannabis brands in 2023 were obtained from a large cannabis e-commerce website. Each description was thematically coded based on the promoted product features.ResultsThe most frequently mentioned product feature was flavor profile and sensation (74.1%), including general and specific flavor descriptors (e.g., fruit flavors) and sensory experiences (e.g., sweet, spicy). Psychoactive effects were noted in 47.5% of the descriptions, detailing potency or effects such as feeling “high,” “stoned,” or “buzzed.” Product quality, such as “purity,” “natural,” or “organic,” was mentioned in 42.3% of the descriptions. Other product benefits described included mood enhancement (33.8%), reduced harm (24.2%), relaxation/tension reduction (23.9%), therapeutic effects (18.4%), focus/creativity (16.6%), convenience/discreetness (11.1%), socialization enhancement (6.7%), and physical performance enhancement (6.1%).DiscussionSome frequently promoted product features of CVPs, such as flavor and sensation and psychoactive effects, may particularly appeal to younger consumers for non-medical use. Meanwhile, attributes surrounding high product quality and reduced harm may reduce individuals’ perceived risks of CVP use and may violate state restrictions on therapeutic claims. Additional research assessing the influence of exposure to marketed product features on product perceptions and use behaviors among priority populations is needed to inform regulations and enforcement.
Vaccination coverage trends in European Union from 1980 to 2020: A joinpoint Regression Analysis
Authors: Causio FA , Villani L , Mariani M , Pastorino R , De Waure C , Ricciardi W , Boccia S 
Published: November 2024

AbstractVaccination coverage trends across European Union (EU) countries have shown concerning variations in recent years. This study aims to analyze vaccination coverage trends for seven key vaccinations across all EU member states from 1980 to 2020. Data from the WHO/UNICEF Estimates of National Immunization Coverage (WUENIC) dataset were analyzed using joinpoint regression models to identify significant changes in vaccination coverage trends. Seven vaccination indicators were examined: DTP-3, HEPB-3, HIB-3, MCV-1, PCV-3, POL-3, and RCV-1. Trends were categorized as positive or negative and significant or not significant, based on Annual Percent Change (APC) calculations. Analysis of 180 joinpoint regressions revealed a predominantly downward trend in vaccination coverage across the EU. 51.7% showed negative performance, while only 37.8% showed positive performance. Only 8 countries (29.6%) demonstrated increased coverage in at least four indicators, while 14 countries (51.8%) showed reduced coverage. The study reveals a concerning decline in vaccination coverage across most EU countries. The findings suggest that mandatory vaccination policies may contribute to maintaining higher coverage rates, though multiple factors influence vaccination uptake, including political, social, and educational determinants.