bluuebunny/crossref_metadata_2025_split huggingface dataset:
Educational interventions delivered to prescribing advisers to influence primary care prescribing: a very low-cost pragmatic randomised trial using routine data from OpenPrescribing.net
Authors: Curtis Helen J , MacKenna Brian , Reddy Bhavana , Walker Alex J , Bacon Sebastian , Perera Rafael , Goldacre Ben 
Published: January 2024

AbstractBackgroundNHS England issued commissioning guidance on 18 low-priority treatments which should not be routinely prescribed in primary care. We aimed to monitor the impact of an educational intervention delivered to regional prescribing advisors by senior pharmacists from NHS England on the primary care spend on low-priority items.MethodsAn opportunistic randomised, controlled parallel-group trial. Participants (clinical commissioning groups, CCGs) were randomised to intervention or control in a 1:1 ratio. The intervention group were invited to participate. The intervention was a one-off educational session. Our primary outcomes concerned the total prescribing of low-priority items in primary care. Secondary outcomes concerned the prescribing of specific low-priority items. We also measured the impact on information-seeking behaviour.Results40 CCGs were randomised, 20 allocated to intervention, with 11 receiving the intervention. There was no significant impact on any prescribing outcomes. There was some possible evidence of increased engagement with data, in the form of CCG email alert sign-ups (p=0.077). No harms were detected.ConclusionsA one-off intervention delivered to CCGs by NHS England did not significantly influence low-priority prescribing. This trial demonstrates how routine interventions planned to improve uptake or adherence to healthcare guidance can be delivered as low-cost randomised trials and how to robustly assess their effectiveness.
Applying the Theoretical Domains Framework to the Implementation of Medicaid Coverage for Doula Services: Doulas’ Perceptions of Barriers and Facilitators in the State of Florida
Authors: Applequist Janelle , Wilson Roneé , Perkins Megan , Evans Victoria , Daniel Angela , Rookwood Averjill , Simmons Elizabeth , Louis-Jacques Adetola F. 
Published: January 2024

AbstractAs of September 2023, more than half of US states have either expanded Medicaid to cover doula care or are in the process of implementing doula care into Medicaid services. However, little is known about doulas’ experiences with Medicaid. Doula uptake of these services is critical to support low-income clients. We explored Florida doulas’ experiences with and perceptions of Medicaid reimbursement. We conducted seven focus groups and seven in-depth interviews with 31 doulas providing perinatal support. An inductive approach to thematic analysis was used to generate primary findings. A deductive approach was then applied to these themes, drawing on the Theoretical Domains Framework to enhance understanding. Most participants reported wanting to engage with Medicaid to support marginalized communities, but not doing so due to misinformation, low reimbursement rates and a complicated reimbursement structure. Doulas that work with Medicaid patients reflected on their frustrations with insurers and the financial impact of delayed reimbursements. Misinformation spread amongst doula communities about the Medicaid reimbursement process (often based on outdated information) was found to be a significant barrier to implementation. Doulas relied on one another for information, as they reported few resources have been made available by Medicaid or managed care organizations for guidance on reimbursement processes. This highlights an opportunity for Medicaid education to ensure client access to doula services and maintenance of the doula workforce. Doula care is associated with better perinatal outcomes, but structural barriers hinder the ability for low-income families to receive doula care in Florida, creating further health inequities.
Glymphatic flow resistance in humans measured by multifrequency impedance dispersion
Authors: Dagum Paul , Giovangrandi Laurent , Levendovszky Swati Rane , Winebaum Jake J. , Singh Tarandeep , Cho Yeilim , Kaplan Robert M. , Jaffee Michael S. , Lim Miranda M. , Vandeweerd Carla , Iliff Jeffrey J. 
Published: January 2024

ABSTRACTGlymphatic function in animal models supports the clearance of brain proteins whose mis-aggregation is implicated in neurodegenerative conditions including Alzheimer’s and Parkinson’s disease. The measurement of glymphatic function in the human brain has been elusive, limiting its potential in translational research. Here we described a non-invasive multimodal device for the first-in-human continuous measurement of sleep-active glymphatic flow resistance using repeated electrical impedance spectroscopy measurements through two separate clinical validation studies. Device measurements successfully (i) paralleled sleep-associated changes in extracellular volume that regulate glymphatic function, (ii) replicated preclinical findings showing glymphatic function is increased with increasing sleep EEG delta power, and is decreased with increasing sleep EEG beta power and heart rate, and (iii) predicted glymphatic solute exchange measured by contrast-enhanced MRI. The present investigational device permits the continuous and time-resolved assessment of glymphatic flow resistance in naturalistic settings necessary to determine the contribution of glymphatic impairment to risk and progression of Alzheimer’s disease, and to enable target-engagement studies that modulate glymphatic function in humans.
Evaluating and Improving the Performance and Racial Fairness of Algorithms for GFR Estimation
Authors: Zhang Linying , Richter Lauren R. , Kim Tevin , Hripcsak George 
Published: January 2024

AbstractData-driven clinical prediction algorithms are used widely by clinicians. Understanding what factors can impact the performance and fairness of data-driven algorithms is an important step towards achieving equitable healthcare. To investigate the impact of modeling choices on the algorithmic performance and fairness, we make use of a case study to build a prediction algorithm for estimating glomerular filtration rate (GFR) based on the patient’s electronic health record (EHR). We compare three distinct approaches for estimating GFR: CKD-EPI equations, epidemiological models, and EHR-based models. For epidemiological models and EHR-based models, four machine learning models of varying computational complexity (i.e., linear regression, support vector machine, random forest regression, and neural network) were compared. Performance metrics included root mean squared error (RMSE), median difference, and the proportion of GFR estimates within 30% of the measured GFR value (P30). Differential performance between non-African American and African American group was used to assess algorithmic fairness with respect to race. Our study showed that the variable race had a negligible effect on error, accuracy, and differential performance. Furthermore, including more relevant clinical features (e.g., common comorbidities of chronic kidney disease) and using more complex machine learning models, namely random forest regression, significantly lowered the estimation error of GFR. However, the difference in performance between African American and non-African American patients did not decrease, where the estimation error for African American patients remained consistently higher than non-African American patients, indicating that more objective patient characteristics should be discovered and included to improve algorithm performance.
A susceptibility gene signature for ERBB2-driven mammary tumor development and metastasis in Collaborative Cross mice with human translational value
Authors: Yang Hui , Wang Xinzhi , Blanco-Gómez Adrián , He Li , García-Sancha Natalia , Corchado-Cobos Roberto , Pérez-Baena Manuel Jesús , Jiménez-Navas Alejandro , Wang Pin , Inman Jamie L , Snijders Antoine M , Threadgill David W , Balmain Allan , Chang Hang , Perez-Losada Jesus , Mao Jian-Hua 
Published: January 2024

AbstractBackgroundDeeper insights into ERBB2-driven cancers are essential to develop novel treatment avenues for ERBB2+ breast cancers (BCs). We employed Collaborative Cross (CC) mouse model, along with human translational evaluation, to unearth genetic factors underpinning Erbb2-driven mammary tumor development and metastasis.Methods732 F1 hybrid female mice between FVB/N MMTV-Erbb2and 30 CC strains were monitored for mammary tumor phenotypes. GWAS pinpointed SNPs that influence various tumor phenotypes. Clinical value of a mouse tumor susceptibility gene signature (mTSGS) was evaluated using public datasets, encompassing TCGA, METABRIC and I-SPY2 cohorts. The predictive power of mTSGS for response to chemotherapy was validatedin vivousing genetically diverse MMTV-Erbb2mice.ResultsDistinct variances in tumor onset, multiplicity, and metastatic patterns were observed across CC strains. Besides lung metastasis, liver and kidney metastases emerged in specific CC strains. GWAS identified 1525 SNPs, 800 SNPs, 568 SNPs, and 23 SNPs significantly associated with tumor onset, multiplicity, lung metastasis, and liver metastasis, respectively. Multivariate analyses flagged SNPs in 20 genes independently tied to various tumor characteristics, designated as mTSGS. These 20 genes were transcriptionally altered in human BCs. We then established mTSGS scores (mTSGSS) based on their transcriptional levels. The mTSGSS showed prognostic values, superseding clinical factors and PAM50 molecular subtype across cohorts. Moreover, mTSGSS predicted pathological complete response (pCR) to six of thirteen treatment regimens, including chemotherapy only, in I-SPY2 study. Importantly, the predictive value of the mTSGSS for pCR stood independent of the MammaPrint score. The power of mTSGSS for predicting chemotherapy response was validated in anin vivoMMTV-Erbb2 model, showing that like findings in human patients, mouse tumors with low mTSGSS were most likely to respond to treatment.ConclusionOur investigation has unveiled many novel genes predisposing individuals to ERBB2-driven cancer. Translational findings indicate that mTSGSS holds promise as a biomarker to refine treatment strategies for BC patients.
The Metabolic Role of MAP3K15: Genetic and Phenotypic Insights from the 23andMe Research Database and Genetics-Driven Recruitment
Authors: Brady Jennifer J. , Kalkus Kira , Nguyen Dominique T. , Wen Jingran , Fuller Zachary L. , Qi Yue , Deering Shaeloren S. , Selcer Leah , Shringarpure Suyash S. ,   The 23andMe Research Team, Holmes Michael V. , Wang Xin 
Published: January 2024

AbstractMAP3K15 has been previously associated with protection from type 2 diabetes (T2D), prompting interest in the development of MAP3K15 inhibitors as a potential therapeutic option for diabetes. The trans-ancestry genome-wide association study (GWAS) meta-analysis and loss-of-function (LoF) burden testing methods that implicate association with T2D greatly benefit from large sample size. The direct-to-consumer genetic testing company, 23andMe, Inc., is the world’s largest research consented genetic database. We leveraged the 23andMe database to further inform the metabolic role of MAP3K15, using a variety of genetic analysis methods. We find that MAP3K15 LoF carriers show a significant delay of 4.5 years in the median age of T2D diagnosis among individuals at high polygenic risk and uncover a novel burden association of MAP3K15 LoF with protection against high cholesterol. We expanded these findings by establishing a capability to recruit consented participants on the basis of genetics unknown to them (specifically, a single LoF variant in MAP3K15, rs148312150), and obtained clinical laboratory evidence of a modest reduction in median cholesterol and LDL/HDL ratio in MAP3K15 LoF carriers. Our findings demonstrate the discovery power of the 23andMe database, including the feasibility of consented participant recruitment to inform therapeutic discovery and development.
Chronicling menstrual cycle patterns across the reproductive lifespan with real world data
Authors: Cunningham Adam C. , Pal Lubna , Wickham Aidan P. , Prentice Carley , Goddard Frederick G. B. , Klepchukova Anna , Zhaunova Liudmila 
Published: January 2024

AbstractBackgroundThe intricate hormonal and physiological changes of the menstrual cycle can influence health on a daily basis. Although prior studies have helped improve our understanding of the menstrual cycle, they often lack diversity in populations included, sample size, and the span of reproductive and life stages. This paper aims to describe the dynamic differences in menstrual cycle characteristics and associated symptoms by age in a large global cohort of period tracking application users. This work aims to contribute to our knowledge and understanding of female physiology at varying stages of reproductive aging.MethodsThis cohort study included self-reported menstrual cycle and symptom information in a sample of Flo application users aged 18-55. Cycle and period length and their variability, and frequency of menstrual cycle symptom logs are described by age of user.ResultsBased on data logged by over 19 million global users of the Flo app, the length of the menstrual cycle and period show clear age associated patterns. With higher age, cycles tend to get shorter (Cycle length: D̄ = 1.85 days, Cohen’s D= 0.59) and more variable (Cycle length SD: D̄ = 0.42 days, Cohen’s D= 0.09), until close to the chronological age (40-44) suggesting menopausal transition, when both cycles and periods become longer (Cycle length: D̄ = 0.86 days, t=48.85, Cohen’s D=0.26; Period length: D̄ =0.08, t=15.6, Cohen’s D=0.07) and more variable (Cycle length SD: D̄ =2.80 days, t=111.43, d=0.51; Period length SD: D̄ =0.23 days, t=67.81, Cohen’s D=0.31). The proportion of individuals with irregular cycles was highest in participants aged 51-55 (44.7%), and lowest in the 36-40 age group (28.3%). The spectrum of common menstrual cycle related symptoms also varies with age. Frequency of logging of cramps and acne is lower in older participants, while logs of headache, backache, stress and insomnia are higher in older users. Other symptoms show different patterns, such as breast tenderness and fatigue peaking between the ages of 20-40, or mood swings being most frequently logged in the youngest and oldest users.ConclusionThe menstrual cycle, and related symptoms are not static throughout the lifespan. Understanding these age related differences in cycle characteristics and symptoms is important in understanding how best to care for and improve daily experience for menstruators across the reproductive life span.
Patient experiences of UK primary care online triage and consultation platforms during COVID-19: A systematic review
Authors: Roberts Christopher , George Jomin , Jenkins Judy 
Published: January 2024

AbstractBackgroundOnline triage and consultation platforms are being increasingly used by healthcare providers in the UK for patient/clinician interactions. COVID-19 accelerated the shift towards the use of these platforms to maintain healthcare provision and reduce transmission. Strict directives were introduced by the UK Government to avoid in-person contact wherever possible in March 2020.AimTo examine patients’ experiences of online triage and consultation in UK primary care during the COVID-19 pandemic and offer considerations for their continued use.DesignThis study follows the PRISMA framework and includes qualitative studies conducted in UK primary care based on the experiences of patient users of any such online platform during the period of March 2020 to April 2023. Studies were included using the PICO format. Three literature databases were searched for relevant studies: PubMed, Science Direct and EMBASE. CASP is used to assess data quality.Results540 studies were reviewed and reduced to 12 studies that met the inclusion criteria. Study characteristics were identified as: year of study, study population, disease types/conditions, patient response themes and the study’s data capture method. A thematic inductive approach identifies three overarching themes (Accessibility, Care delivery, System functionality) and 10 sub-themes (Affordability, IT literacy, Communication, Convenience, Care quality, Patient safety/privacy, Usability, Continuity of care, Inequality and Media influence).ConclusionThis review highlights aspects of patient satisfaction and benefit but also those most concerning for patients. This study reviews the rapid, compulsory adoption of these systems during COVID-19 with implications for their future implementation beyond the pandemic.
Towards patient-relevant, trial-ready digital motor outcomes for SPG7: a cross-sectional prospective multi-center study (PROSPAX)
Authors: Beichert Lukas , Seemann Jens , Kessler Christoph , Traschütz Andreas , Müller Doreen , Dillmann-Jehn Katrin , Ricca Ivana , Satolli Sara , Nazli Başak Ayşe , Coarelli Giulia , Timmann Dagmar , Gagnon Cynthia , van de Warrenburg Bart P. ,   PROSPAX consortium, Ilg Winfried , Synofzik Matthis , Schüle Rebecca 
Published: January 2024

AbstractBackground and ObjectivesWith targeted treatment trials on the horizon, identification of sensitive and valid outcome measures becomes a priority for the >100 spastic ataxias. Digital-motor measures, assessed by wearable sensors, are prime outcome candidates for SPG7 and other spastic ataxias. We here aimed to identify candidate digital-motor outcomes for SPG7 – as one of the most common spastic ataxias – that: (i) reflect patient-relevant health aspects, even in mild, trial-relevant disease stages; (ii) are suitable for a multi-center setting; and (iii) assess mobility also during uninstructed walking simulating real-life.MethodsCross-sectional multi-center study (7 centers, 6 countries). Unaided walking was assessed in 65 patients with SPG7 and 50 unrelated healthy controls using 3 wearable sensors (Opal APDM). Digital gait measures were correlated to measures of disease severity (SARA, SPRS; including mobility-relevant subscores SPRS1315291292025, SARAPG) and activities of daily living (FARS-ADL). The task set included lab-based defined gait tasks, complemented by uninstructed ‘supervised free walking’.ResultsAmong 30 hypothesis-based gait measures, 18 demonstrated at least moderate effect size (Cliff’s δ>0.5) in discriminating SPG7 patients from controls, and 17 even in mild disease stages (SPRSmobility≤9). Spatiotemporal variability measures such as the spatial variability composite measure SPcmp (ρ=0.67, p=<0.0001), Stride Time CV (ρ=0.67, p=<0.0001) and Swing CV (ρ=0.64, p=<0.0001) showed the highest correlations with clinician-reported mobility scores (SPRSmobility), and overall disease severity (SPRS, SARA). Overall, top-ranked measures also correlated with patient-relevant functional deficits in everyday life activities (FARS-ADL). In mild disease stages (SPRSmobility≤9, n=41), Swing CV (ρ=0.53, p=<0.0001) and SPcmp (ρ=0.50, p=<0.0001) correlated with SPRSmobility. In the uninstructed ‘supervised free walking’ task, the correlations between spatiotemporal variability measures (Stride Time CV, Stride Length CV, Swing CV) and SPRSmobilitycould be confirmed; additionally, Gait Speed (ρ=-0.59, p=<0.0001) was highly correlated with SPRSmobility.DiscussionWe here identified trial-ready digital-motor candidate outcomes for the spastic ataxia SPG7, all characterized by proven multi-center applicability, ability to discriminate patients from controls, and correlation with measures of disease severity – even in mild disease stages –, and patient-relevant everyday function. If validated longitudinally, these sensor outcomes might inform future natural history and treatment trials in SPG7 and other spastic ataxias.
Development and Validation of an Intrinsic Capacity Score in the UK Biobank Study
Authors: Beyene Melkamu Bedimo , Visvanathan Renuka , Ahmed Muktar , Benyamin Beben , Beard John R. , Amare Azmeraw T. 
Published: January 2024

AbstractBackgroundIn 2015, the World Health Organization introduced the concept of intrinsic capacity (IC) to define the individual-level characteristics that enable an older person to be and do the things they value. This study developed an IC score for UK Biobank (UKB) study participants and validated its use as a tool for health outcome prediction, understanding healthy aging trajectories, and genetic research.MethodsOur analysis included data from 45,208 UKB participants who had a complete record of the ten variables included in the analysis. Factor adequacy was tested using Kaiser–Meyer– Olkin (KMO), Barthelt’s, and the determinant of matrix tests, and the number of factors was determined by the parallel analysis method. Exploratory and confirmatory factor analyses were employed to determine the structure and dimensionality of indicators. Finally, the IC score was generated, and its construct and predictive validities as well as reliability were assessed.ResultsThe factor analysis identified a multidimensional construct comprising, one general factor (IC) and five specific factors (locomotor, vitality, cognitive, psychological, and sensory). The bifactor structure showed a better fit (comparative fit index = 0.995, Tucker Lewis index = 0.976, root mean square error of approximation = 0.025, root mean square residual = 0.009) than the conventional five-factor structure. The IC score, generated using the bifactor confirmatory factor analysis has good construct validity, as demonstrated by an inverse association with age (lower IC in older age; beta = -0.035 (95%CI: -0.036, -0.034)), frailty (lower IC score in prefrail, beta = -0.104 (95%CI: (-0.114, -0.094) and frail, beta = -0.227 (95%CI: -0.267, -0.186) than the robust), and Charlson’s comorbidity index (CCI) for incident cases (a lower IC score associated with increased CCI, beta, = -0.019 (95%CI: -0.022, -0.015). The IC score also predicted incident CCI (a one-unit increase in baseline IC score led to lower CCI, beta = 0.147 (95%CI: -0.173, - 0.121)).ConclusionThe bifactor structure showed a better fit in all goodness of fit tests. The IC construct has strong structural, construct, and predictive validities and it is a promising tool for monitoring aging trajectories.HighlightsLarge biobank studies should be leveraged with intrinsic capacity scores developed.Factor analysis confirmed IC as one general factor and five specific factors.Better goodness of fit observed with the bifactor model versus conventional structure.The bifactor model IC score has a good construct and predictive validity.The IC score can be used as a single reliable measure of IC.