Glenohumeral subluxation (GHS) is a common rehabilitation challenge in the hemiplegic upper limb following stroke, potentially leading to shoulder pain, secondary brachial plexus injury and various other complications. While electrical stimulation therapies, such as electromyography biofeedback, electroacupuncture and neuromuscular electrical stimulation, have shown promise in managing GHS, some controversy remains. Although clinical trials and meta-analyses have confirmed the efficacy of these therapies, healthcare professionals have yet to reach a consensus on which specific therapy is most effective for reducing shoulder subluxation (SS), alleviating pain and improving quality of life. This study will perform a network meta-analysis to compare the relative efficacy of different electrical stimulation therapies for treating GHS in patients following acute stroke.

We will systematically search the following databases: PubMed, MEDLINE, Embase, Cochrane Library, Web of Science, Chinese biomedical literature database (SinoMed), Wanfang databases (Wanfang), VIP Journal Integration Platform (VIP) and China National Knowledge Infrastructure (CNKI). Our search will cover the period from the inception of each database until 14 April 2025, and will be restricted to studies published in Chinese or English. The primary outcomes of interest will be the degree of improvement in SS, improvements in quality of life and reductions in pain. We will conduct pairwise meta-analyses using RevMan V.5.3 (The Cochrane Collaboration, Copenhagen, Denmark), and network meta-analyses using ADDIS V.1.16.6 (Drugis, Groningen, The Netherlands) and Stata V.16.0 (StataCorp, College Station, Texas, USA) to compare the relative efficacy of different electrical stimulation therapies. Screening, data extraction, risk of bias assessment and evaluation of the certainty of evidence will all be performed independently by two reviewers to ensure accuracy and reliability. The risk of bias within individual studies will be assessed using the Cochrane Risk of Bias 2 (ROB 2) tool, and the certainty of evidence will be evaluated using the Grading of Recommendations Assessment, and Evaluation (GRADE) and Confidence in Network Meta-Analysis (CINeMA) frameworks to ensure transparency and methodological rigour.

Ethical approval is not required for this study. The findings will be submitted to a peer-reviewed journal or conference.

CRD42024541228.

To explore existing evidence for the provision of support for return to work (RTW) in long COVID (LC) patients and the barriers and facilitators to taking up this support.

A rapid review reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The study was preregistered in PROSPERO (ID: CRD42023478126).

Searches were completed in June 2024 across major databases including MEDLINE, Embase, PsycINFO, evidence-based medicine reviews, Web of Science and Google Scholar.

Included studies focused on people with LC (PwLC) symptoms lasting over 12 weeks and addressed either: (1) non-workplace- or workplace-based support for RTW and/or (2) barriers and facilitators to RTW in this population.

A quality assessment was conducted using the JBI Systematic Reviews critical appraisal tool. The data were summarised in tabular format and a narrative synthesis.

Twenty-five studies were included. While many studies demonstrated rigorous methodologies and low risk of bias levels, some had high and medium risk levels. Non-workplace-based support was mostly measured quantitatively and included interdisciplinary healthcare programmes, clinical interventions and rehabilitation programmes focusing on pacing and breathing strategies. Compensation and insurance schemes were important funders of these interventions.

Workplace-based support was mostly measured qualitatively. Barriers to the provision of support at organisational level included lack of understanding of LC symptoms, insufficient workplace guidance and educational gaps among managers. Individual barriers included threat of income loss, remote working and disconnection from the workplace. Facilitators for support included recognition and validation of LC and its symptoms, and eligibility for disability benefits associated with work.

RTW is an important outcome of health-related absence and should be systematically recorded in studies of PwLC. The heterogeneity and unpredictability of LC symptoms create challenges for supporting working age populations. Further research is crucial to better understand the specific RTW needs for PwLC and address potential barriers and facilitators to workplace-based support, particularly through interventions, organisational practices and employ-led policies that enable sustained RTW. Consistent guidelines on LC’s definition and disability status may facilitate the provision of support and the development of interventions.

CRD42023478126.

Suicide has a substantial economic burden, yet in low- or middle-income countries, the analysis is constrained by the absence of disaggregated economic data. Existing studies primarily rely on gross costing methods, overlooking sector-specific costs such as healthcare, law enforcement and productivity losses at the family and community levels. Furthermore, the societal perspective, essential for understanding the multifaceted economic impacts of suicide, remains insufficiently explored in the Indian context.

This study will quantify the economic burden of suicide and suicide attempts in India. The initial phase will involve developing a resource-use measure instrument to capture the societal costs associated with suicide. Subsequently, a retrospective, incidence-based study will be conducted in India using the developed instrument. This will also be complemented with the Health-Related Quality of Life Assessment using EuroQOL (EQ-5D-5L). Exploratory subgroup cost comparisons (eg, suicide methods, healthcare facility type) will use non-parametric tests, including the Mann–Whitney U and Kruskal–Wallis tests. Generalised linear mixed models (gamma distribution and log link) will analyse longitudinal cost and HRQoL data, besides bootstrapping techniques and sensitivity analyses.

Ethics approval was obtained for the study from the institutional review board of the Indian Law Society (ILS/141/2024). All data collected during the study will adhere to ethical guidelines, taking informed consent. The findings of the study will be disseminated through peer-reviewed journals to aid data-driven decision-making.

Clinical Trial Registry of India (CTRI/2025/02/080380), date of registration: 12/02/2025.

To develop and compare algorithms for identifying gestational diabetes mellitus (GDM) across European electronic healthcare databases and evaluate their impact on the estimated prevalence.

Multi-national cohort study using routinely collected electronic healthcare data

National and regional databases in five European countries (Norway, Finland, Italy, Spain and France), in primary and/or secondary care.

Pregnancy cohorts resulting in stillbirths or live births between 2009 and 2020, comprising 602 897 pregnancies in Norway, 507 904 in Finland, 374 009 in Italy, 193 495 in Spain and 116 762 in France.

The primary outcome was the prevalence of GDM identified using six algorithms: (1) Only diagnosis; (2) Diagnosis or prescription; (3) Two diagnoses or prescriptions (2DxRx); (4) Diagnosis including unspecified diabetes in pregnancy or prescription (DxRx broad); (5) Diagnosis excluding pre-existing diabetes in pregnancy or prescription; (6) Registration of GDM in a birth registry (BR).

The strictest algorithm (2DxRx) resulted in the lowest GDM prevalence, while the broadest (DxRx broad) resulted in the highest, except in France where it was BR. In the Nordic countries, GDM prevalence varied only slightly by algorithm; greater variations were observed in other countries. The prevalence ranged from 3.5% (95% CI: 3.5% to 3.5%) to 4.6% (95% CI: 4.5% to 4.7%) in Norway; 12.1% (95% CI: 12.0% to 12.2%) to 15.8% (95% CI: 15.7% to 15.9%) in Finland, where prevalence was much higher than elsewhere. The prevalence ranged from 1.3% (95% CI: 1.3% to 1.3%) to 5.4% (95% CI: 5.3% to 5.5%) in Italy; 1.6% (95% CI: 1.5% to 1.7%) to 6.2% (95% CI: 6.1% to 6.3%) in Spain; and 1.7% (95% CI: 1.6% to 1.8%) to 5.8% (95% CI: 5.7% to 5.9%) in France.

In this multinational study, GDM prevalence ranged from 1.3% to 15.8% depending on the algorithm and database. Nordic countries showed smaller differences in prevalence between algorithms, while the other countries showed larger variations, likely due to differences in coding practices, healthcare systems and database coverage.

This study aims to develop a methodology to retrieve, harmonise and evaluate the completeness of national body mass index (BMI) data from linked electronic health record (EHR) sources to build a longitudinal research-ready data asset (RRDA).

A longitudinal study of BMI records spanning 23 years (1 January 2000 to 31 December 2022) from four data sources.

The national BMI RRDA is created within the Secure Anonymised Information Linkage (Databank), encompassing the entire population of Wales, UK.

We built a methodology that provides a reproducible framework for extracting and harmonising BMI data from four major linked EHRs across two age groups: children and young people (CYP; 2–18 years old) and adults (19 years and older). The methodology is adaptable across different trusted research environments. We evaluated the completeness and retention of records over 1-, 5- and 23-year periods by calculating the proportion of missing data relative to each year’s population.

We retrieved 53.4 million records for 3.2 million individuals across Wales from 1st January 2000 to 31 December 2022. Among these, 3% of CYP and 34% of adults had repeat BMI measurements recorded over periods ranging from 5 to 23 years. Throughout the entire population of Wales during this period, 49% of CYP and 26% of adults had at least one BMI reading recorded, resulting in a missingness rate of 51% for CYP and 74% for adults. Preserving BMI information by retaining the most recently recorded BMI over 1-, 5- and 23-year intervals from 2022 showed coverage rates of 10%, 33% and 68%, respectively, for CYP, and 25%, 51% and 73%, respectively, for adults.

Our findings highlight substantial variations in BMI data availability and retention across CYP and adults, as well as time periods within EHR in Wales. Wider adoption of this approach can enhance standardised approaches in using accessible measures like BMI to assess disease risk in population-based studies, strengthening public health initiatives and research efforts.

To investigate whether gestational diabetes mellitus (GDM) was associated with survival without bronchopulmonary dysplasia (BPD) in very preterm infants (VPIs).

Retrospective multicentre cohort study.

A total of 79 neonatal intensive care units across China, January 2019 to December 2021.

A total of 23 752 VPIs (

The primary outcomes are survival without BPD at 36 weeks’ postmenstrual age (PMA) and its components.

Infants exposed to GDM were associated with a higher rate of survival without BPD (aOR 1.12, 95% CI 1.04 to 1.21) at 36 weeks PMA and lower mortality (aOR 0.75, 95% CI 0.64 to 0.84) before 36 weeks PMA than unexposed infants. However, no significant association was observed between GDM and BPD at 36 weeks PMA (aOR 0.94, 95% CI 0.87 to 1.02), respiratory distress syndrome, need for advanced resuscitation or mechanical ventilation. After propensity score matching, GDM-exposed VPIs maintained higher survival without BPD (aOR 1.13, 95% CI 1.02 to 1.26) and lower mortality (aOR 0.81, 95% CI 0.68 to 0.97). These associations were strongest in infants born before 28 weeks (aOR 1.32, 95% CI 1.11 to 1.57) and those small for gestational age (aOR 1.41, 95% CI 1.11 to 1.80).

GDM was not associated with worsened BPD in VPIs. The positive association with survival and survival without BPD warrants could reflect a selection bias.

The increasing volume of radiological images and the associated workload of report generation necessitate efficient solutions, making artificial intelligence (AI) a crucial tool to streamline this process for radiologists. Recent years have seen a surge in research exploring AI-driven radiological report generation directly from images, particularly with the emergence of large vision language models. However, a comprehensive understanding of the current landscape, including specific limitations and the extent to which efforts move beyond abnormality detection to full textual report generation, remains unclear. This scoping review aims to systematically map the existing literature to provide an overview of the current state of AI in generating radiological reports from medical images, including the scope and limitations of existing research. To our knowledge, no prior scoping review has comprehensively mapped this landscape, especially considering recent advancements in foundation models in medicine and related AI architectures. Considering the explosive growth of related studies in recent years, a comprehensive scoping review will be significant in mapping the current research status and understanding relevant limitations.

This scoping review will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews guidelines to map the literature on AI generating radiological reports from medical images. We will search PubMed, Scopus and Web of Science for peer-reviewed articles (January 2016 to March 2025) using keywords related to AI, radiological reports and medical images. Original research in English focusing on AI-driven report generation from images will be included and studies without report generation or not using medical images as input will be excluded. Two independent reviewers will perform a two-stage screening. Data extraction, guided by the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies checklist and focusing on study characteristics, AI methods, image modalities, report features, limitations and key findings, will be analysed using narrative and descriptive synthesis, with results presented in tables, figures and a narrative summary.

This protocol describes a scoping literature review methodology that does not involve research on humans, animals or their data; therefore, no ethical approval is required. Following the review, the results will be considered for publication in a relevant peer-reviewed journal and may be shared with stakeholders through reports or summaries.

Anxiety and depression are among the top contributors to disability in the Kingdom of Saudi Arabia (KSA), but little is known about their economic impact. This study estimates the economic burden of moderate to severe symptoms of anxiety and depression among adults in KSA.

A cross-sectional survey was administered via a web panel. Participants were asked to fill out the Patient Health Questionnaire-4 (PHQ-4) for themselves and on behalf of other household members to capture prevalence rates. Those who reported at least moderate symptoms of anxiety or depression filled out a longer survey with questions on healthcare utilisation and productivity losses due to symptoms. These responses were monetised using prevalence rates and population estimates to calculate per-person and total annual costs.

Prevalence estimates are based on responses from 1164 participants on behalf of 3202 Saudi adults. Of these, 269 individuals with symptoms completed the longer survey.

Prevalence of anxiety and depression; healthcare utilisation (medications, outpatient, inpatient) and productivity losses due to absenteeism and presenteeism.

In total, 26.2% reported at least moderate symptoms consistent with anxiety and/or depression. Among those with symptoms, direct healthcare costs due to anxiety and depression averaged Saudi riyal (SAR) 3431.95 per person annually. Indirect costs via absenteeism and presenteeism averaged SAR 9702.87 and SAR 24 577.28 per person assuming that anxiety and/or depression episodes last for 6 months. Summing up the healthcare costs and productivity losses yields a total annual economic burden of SAR 163.3 billion. Absenteeism accounts for 24.8% of this total (SAR 40.5 billion), presenteeism accounts for 62.8% (SAR 102.5 billion) and healthcare resource utilisation accounts for 12.4%(SAR 20.3 billion).

The overall prevalence of anxiety and depression in KSA is estimated at 26.2%. The economic burden associated with these symptoms amounts to SAR 163.3 billion or 4.1% of GDP. Absenteeism and presenteeism costs account for the vast majority of the total, but a large percentage (nearly 60%) also report emergency department visits and unplanned hospital admissions. Evidence-based interventions should be considered to address the health and economic burden of these conditions in KSA.

Multiple sclerosis (MS) is a chronic neurological condition that affects approximately 150 000 people in the UK and presents a significant healthcare burden, including the high costs of disease-modifying treatments (DMTs). DMTs have substantially reduced the risk of relapse and moderately reduced disability progression. Patients exhibit a wide range of responses to available DMTs. The Predicting Optimal INdividualised Treatment response in MS (POINT-MS) cohort was established to predict the individual treatment response by integrating comprehensive clinical phenotyping with imaging, serum and genetic biomarkers of disease activity and progression. Here, we present the baseline characteristics of the cohort and provide an overview of the study design, laying the groundwork for future analyses.

POINT-MS is a prospective, observational research cohort and biobank of 781 adult participants with a diagnosis of MS who consented to study enrolment on initiation of a DMT at the Queen Square MS Centre (National Hospital of Neurology and Neurosurgery, University College London Hospital NHS Trust, London) between 01/07/2019 and 31/07/2024. All patients were invited for clinical assessments, including the expanded disability status scale (EDSS) score, brief international cognitive assessment for MS and various patient-reported outcome measures (PROMs). They additionally underwent MRI at 3T, optical coherence tomography and blood tests (for genotyping and serum biomarkers quantification), at baseline (i.e., within 3 months from commencing a DMT), and between 6–12 (re-baseline), 18–24, 30–36, 42–48 and 54–60 months after DMT initiation.

748 participants provided baseline data. They were mostly female (68%) and White (75%) participants, with relapsing–remitting MS (94.3%), and with an average age of 40.8 (±10.9) years and a mean disease duration of 7.9 (±7.4) years since symptom onset. Despite low disability (median EDSS 2.0), cognitive impairment was observed in 40% of participants. Most patients (98.4%) had at least one comorbidity. At study entry, 59.2% were treatment naïve, and 83.2% initiated a high-efficacy DMT. Most patients (76.4%) were in either full- or part-time employment. PROMs indicated heterogeneous impairments in physical and mental health, with a greater psychological than physical impact and with low levels of fatigue. When baseline MRI scans were compared with previous scans (available in 668 (89%) patients; mean time since last scan 9±8 months), 26% and 8.5% of patients had at least one new brain or spinal cord lesion at study entry, respectively. Patients showed a median volume of brain lesions of 6.14 cm³, with significant variability among patients (CI 1.1 to 34.1). When brain tissue volumes z-scores were obtained using healthy subjects (N=113, (mean age 42.3 (± 11.8) years, 61.9% female)) from a local MRI database, patients showed a slight reduction in the volumes of the whole grey matter (–0.16 (–0.22 to –0.09)), driven by the deep grey matter (–0.47 (–0.55 to –0.40)), and of the whole white matter (–0.18 (–0.28 to –0.09)), but normal cortical grey matter volumes (0.10 (0.05 to 0.15)). The mean upper cervical spinal cord cross-sectional area (CSA), as measured from volumetric brain scans, was 62.3 (SD 7.5) mm². When CSA z-scores were obtained from the same healthy subjects used for brain measures, patients showed a slight reduction in CSA (–0.15 (–0.24 to –0.10)).

Modelling with both standard statistics and machine learning approaches is currently planned to predict individualised treatment response by integrating all the demographic, socioeconomic, clinical data with imaging, genetic and serum biomarkers. The long-term output of this research is a stratification tool that will guide the selection of DMTs in clinical practice on the basis of the individual prognostic profile. We will complete long-term follow-up data in 4 years (January 2029). The biobank and MRI repository will be used for collaborative research on the mechanisms of disability in MS.

As care needs increase in complexity, a shift to people-centred, integrated care is required to meet the full range of health and social care needs of clients. However, limited opportunities exist for care providers to develop interprofessional competencies as part of pre-licensing and/or continuing education. New learning models, such as case-based learning (CBL), that facilitate the development of interprofessional competencies and are aligned with practice realities of providers are needed. This scoping review will collate and codify leading practices and knowledge gaps in the development and delivery of CBL programmes in pre-licensing and continuing education for health and social care providers.

A scoping review will be conducted in accordance with the Joanna Briggs Institute methodology for scoping reviews and reported using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews checklist. MEDLINE, CINAHL Plus, Scopus, ERIC Institute of Education Sciences, PsycINFO and Education Source will be searched for peer-reviewed literature; Google Scholar, ProQuest Dissertations and Web of Science will be searched for grey literature. Reference lists of full-text scholarly sources, key journals and authors will be searched manually. Study selection and extraction will be conducted by two independent reviewers. English sources published between 2014 and 2024 that discuss CBL epistemologies, characteristics, delivery mechanisms, programme limitations and/or programme evaluation in health and social care pre-licensing and/or professional training will be included. Data will be analysed using directed content analysis and synthesised as a narrative summary.

This scoping review protocol was reviewed by the Southlake Health Research Ethics Board and received ethics exemption. Findings will be disseminated through peer-reviewed publication, conferences, professional networks and social media, and used to inform the development of an evidence-based training programme for health and social care providers.

Open Science Framework https://doi.org/10.17605/OSF.IO/6YXHN

Mentalising, the capacity to understand behaviour via underlying mental states, is a key construct in psychopathology. While self-report instruments are widely used to assess mentalising, significant questions about their psychometric properties persist and no systematic review has comprehensively evaluated them using standardised criteria. This systematic review, guided by the COnsensus-based Standards for the selection of health Measurement Instruments () methodology, aims to: (1) identify all available self-report mentalising measures for adults; (2) evaluate the methodological quality of their validation studies; (3) synthesise and grade the quality of evidence on their measurement properties and (4) provide evidence-based recommendations for their use in research and clinical practice.

Five electronic databases (SCOPUS, Web of Science, PsycINFO, PubMed, ProQuest) will be searched from their inception, supplemented by a search of grey literature and reference lists. We will include studies of any design that report on at least one measurement property of a self-report measure of mentalising in adults. Two reviewers will independently screen all records, extract relevant data and assess the methodological quality of included studies using the COSMIN Risk of Bias checklist. For each instrument, the evidence for each measurement property will be synthesised, and the overall quality of the evidence will be graded using a modified Grading of Recommendations Assessment, Development and Evaluation approach.

As this systematic review will synthesise data from previously published studies, it does not require formal ethical approval. The findings will be disseminated through a peer-reviewed, open-access publication and presentations at scientific conferences. The results will provide a comprehensive inventory of available measures and a rigorous evaluation of their psychometric quality, creating an evidence base to guide clinicians and researchers in selecting the most appropriate instruments for mentalising assessment.

CRD420251031469.