Urgent and emergency care in Germany is delivered across multiple, loosely connected sectors. In the absence of coherent, time-resolved data on patient movements between emergency medical services (EMS), out-of-hours ambulatory care, emergency departments (EDs) and inpatient care, inefficiencies and coordination gaps remain difficult to quantify. A process-centric, trans-sectoral analysis is required to characterise real-world patient pathways and identify actionable levers for improvement. The study aims to reconstruct, model and analyse patient pathways for urgent health complaints across all relevant sectors of the healthcare system in a German model region.

We will employ a mixed-methods observational study design. Routine data from EMS, out-of-hours ambulatory care, EDs and subsequent inpatient care will be pseudonymised at source, linked via a trusted third party and analysed within a trusted research environment. Time-stamped event logs will support process mining for discovery, conformance and performance analysis alongside descriptive statistics with stratification by context, such as setting, time of day, urgency and patient cohorts. Anonymous cross-sectional surveys of patients and front-line professionals, complemented by quarterly snapshot surveys in out-of-hours ambulatory care and interviews, will provide convergent evidence on the motives, barriers and coordination of utilisation behaviour. Enrolment for surveys is anticipated from the fourth quarter of 2025; routine data capture covers 1 January–31 December 2026; analyses and dissemination run until 31 December 2027.

The study received ethical approval from the Ethics Committee of the Medical Faculty at RWTH Aachen University (EK 25-351). Survey modules are conducted anonymously with voluntary participation and without collection of direct identifiers; routine care data are processed in pseudonymised form and analysed within a trusted research environment. Stakeholder interviews will be conducted with informed consent. Results will be disseminated through peer-reviewed publications, conference presentations and summary reports for participating institutions and stakeholders, complemented by plain-language materials to support patient-centred navigation.

DRKS00035916.

Timely publication of preregistered study outcomes is not self-evident. Discrepancies can lead to significant research waste.

To assess timely (within 7 years) and consistent publication of preregistered primary outcomes and associated factors of total knee arthroplasty (TKA) studies registered between 2000 and 2017 over time.

An observational study.

ClinicalTrials.Gov, MEDLINE, Embase, Cochrane Library, Web of Science, PubMed and Google Scholar.

Registered TKA trials at ClinicalTrials.Gov between 2000 and 2017.

ClinicalTrials.Gov’s required and optional data elements for registering a study and the preregistered and published primary outcome, defined as the outcome stated in the primary outcome field on ClinicalTrials.Gov. We used descriptive statistics, Kaplan-Meier curves and Cox regression analyses.

1352 registered TKA (1072 interventional; 280 observational) studies were included, with 967 (811 interventional; 156 observational) unique references. Regarding the publication of preregistered primary outcomes within 7 years, the results for interventional trials were 0% (2000), which increased to 59.6% (2017). Observational studies were timely published in 0% (2000) and 37.5% (2017). Interventional trials and observational studies not funded by industry were more likely to have timely and consistent publication of their primary outcomes. Drug intervention trials were more likely to be timely and consistently published than procedure-focused trials. Phase 3 interventional trials were more likely, while phase 1 trials were less likely to be consistently published on time.

Despite ongoing efforts to improve publication rates, over a third of interventional trials remain unpublished within 7 years. For observational studies, the rate is even lower, with only two-fifths published on time, contributing to significant research waste.

CRD42021246599.

Acute coronary syndrome (ACS) is the leading cause of morbidity and mortality among individuals with cardiovascular disease, accounting for half of all global cardiovascular-related deaths. No prior research has examined ACS treatment outcomes and associated factors in the study area. This study aimed to evaluate the risk factors and treatment outcome of ACS patients admitted to public hospitals in Harari Regional State, Eastern Ethiopia.

A retrospective hospital-based cross-sectional study was conducted among 308 ACS patients. Patient records from admissions between 1 November 2018 and 31 October 2023 were reviewed, with data collected between 10 January and 10 February 2024 using a structured checklist adapted from previous research. Statistical analysis was performed using SPSS V.25.0, with bivariable and multivariable logistic regression identifying significant associations at a p value

The mean patient age was 56.4±16 years, with males comprising 77.3% of participants. Half (51.6%) resided in rural areas, and only 16.2% presented within 12 hours of symptom onset. Overall, 81 patients (26.3%) experienced a poor treatment outcome for ACS, including 39 (12.7%) in-hospital deaths, 24 (7.8%) referrals to higher-level facilities and 18 (5.8%) who left against medical advice. Factors significantly associated with poor outcome included hospital presentation more than 72 hours after symptom onset (AOR 2.734 (95% CI 1.006 to 7.435)), left ventricular ejection fraction (LVEF)

Poor treatment outcome was independently predicted by the presence of ischaemia features on the echocardiography, LVEF (

Longitudinal studies provide insights into the outcomes of medical training curriculum. However, few educational cohort studies have been conducted in Iran. This study aims first to evaluate the impact of the current curriculum on medical students' medium- and long-term academic and career outcomes and, second, to identify medical students' characteristics and how they change through the doctor of medicine programme.

This protocol outlines a multi-phase, prospective cohort study that will take place in Mashhad, Iran. The study will implement the Kirkpatrick model, investigating medical students' knowledge, skills, behaviour and professionalism development over 10 years. Approximately 1000 medical students will be recruited through peer invitations and social networks. Data will be collected through baseline and follow-up questionnaires, academic performance records and comprehensive test scores throughout the Doctor of Medicine (MD) programme.

The data from the questionnaires will be reported using a Likert scale. Quantitative data will be described using means and SD, while qualitative variables will be presented as frequencies and percentages. We will evaluate the relationship between quantitative variables using correlation coefficients and the relationship between qualitative variables via the ² or Fisher exact test. All tests will be two-sided, with a significance level set as p

All participants will complete written informed consent before data collection. All students can withdraw from the study at any time with no consequences. Results of this study will be presented at relevant conferences and will be submitted for publication in peer-reviewed journals. This study was approved by the Ethics Committee of Mashhad University of Medical Sciences.

IR.MUMS.REC.1400.311.

Intermittent physiological monitoring and early warning scores (EWS) are limited in their ability to detect deteriorating patients in a timely manner. Wearable physiological sensors allow continuous remote monitoring and may be more timely and accurate in the identification of those at risk, compared with manual collection. This study aims to determine if wearable physiological sensors can be used for the early detection of postoperative deterioration, while being acceptable to patients and healthcare staff.

This is a prospective observational cohort study that will recruit adults undergoing major surgery in Benin, India, Ghana, Guatemala, Mexico, Nigeria, Rwanda and the UK. Participants will wear wearable physiological chest and limb sensors before, during and after surgery for up to 10 days or until discharge. In this ‘shadow-mode’ study, continuous physiological observations collected using the devices will not be made available to clinical teams. No changes in participant care will result. Standard of care clinical data will be collected contemporaneously. Continuous sensor data will be used to design algorithms to predict deterioration and specific complications in this population. Usability and feasibility testing, through focus groups, interviews and questionnaires, will be undertaken with healthcare professionals and people undergoing surgery.

Our stakeholder panel are directly involved in all aspects of this study, which will be conducted in accordance with the principles of the International Conference on Harmonisation Tripartite Guideline for Good Clinical Practice (ICH GCP) in addition to the principles of the ethics committee(s)/Institutional Review Boards (IRBs) who have reviewed and approved this study. Artificial intelligence (AI) prediction models will be reported in accordance with the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis+Artificial Intelligence (TRIPOD+AI) and Developmental and Exploratory Clinical Investigations of DEcision support systems driven by Artificial Intelligence (DECIDE-AI) reporting guidelines frameworks.

NCT06565559.

Women with gestational diabetes mellitus (GDM) are at seven-fold to ten-fold increased risk of type 2 diabetes mellitus (T2DM) when compared with those who experience a normoglycaemic pregnancy, and the cumulative incidence increases with the time of follow-up post birth. This protocol outlines the development and validation of a risk prediction model assessing the 5-year and 10-year risk of T2DM in women with a prior GDM diagnosis.

Data from all birth mothers and registered births in Victoria and South Australia, retrospectively linked to national diabetes data and pathology laboratory data from 2008 to 2021, will be used for model development and validation of GDM to T2DM conversion. Candidate predictors will be selected considering existing literature, clinical significance and statistical association, including age, body mass index, parity, ethnicity, history of recurrent GDM, family history of T2DM and antenatal and postnatal glucose levels. Traditional statistical methods and machine learning algorithms will explore the best-performing and easily applicable prediction models. We will consider bootstrapping or K-fold cross-validation for internal model validation. If computationally difficult due to the expected large sample size, we will consider developing the model using 80% of available data and evaluating using a 20% random subset. We will consider external or temporal validation of the prediction model based on the availability of data. The prediction model’s performance will be assessed by using discrimination (area under the receiver operating characteristic curve, calibration (calibration slope, calibration intercept, calibration-in-the-large and observed-to-expected ratio), model overall fit (Brier score and Cox-Snell R2) and net benefit (decision curve analysis). To examine algorithm equity, the model’s predictive performance across ethnic groups and parity will be analysed. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis-Artificial Intelligence (TRIPOD+AI) statements will be followed.

Ethics approvals have been received from Deakin University Human Research Ethics Committee (2021–179); Monash Health Human Research Ethics Committee (RES-22-0000-048A); the Australian Institute of Health and Welfare (EO2022/5/1369); the Aboriginal Health Research Ethics Committee of South Australia (SA) (04-23-1056); in addition to a Site-Specific Assessment to cover the involvement of the Preventative Health SA (formerly Wellbeing SA) (2023/SSA00065). Project findings will be disseminated in peer-reviewed journals and at scientific conferences and provided to relevant stakeholders to enable the translation of research findings into population health programmes and health policy.

The goal of the study was to determine the magnitude and contributing factors of low back pain among primary school teachers in Borama Town, Somaliland.

An institution-based descriptive cross-sectional study design was employed. Simple random sampling was used to select the study units from each school.

The study was conducted in Borama, Somaliland.

A total of 268 primary school teachers participated in the study.

The primary outcome of the study was the prevalence of low back pain.

The study found that 51.5% of school teachers had low back pain. There was a strong link between low back pain and having a higher Body Mass Index (adjusted OR (AOR)=2.63) and stress at work (AOR=3.34). Sleep disturbance (AOR=1.73), lifting heavy materials (AOR=1.67) and a history of low back injury (AOR=2.12) were also significant predictors of low back pain.

More than half of primary school teachers had low back pain over the past 12 months. Higher Body Mass Index, history of low back injury, stress at work, lifting heavy material and sleep disturbance were significant and independent predictors of low back pain among primary school teachers.

Cystic fibrosis (CF) is a genetic condition of impaired membrane electrolyte transport and is characterised by defects in the production and function of the cystic fibrosis transmembrane conductance regulator (CFTR) protein. Ground-breaking CFTR modulator therapy has resulted in a notable shift in the clinical presentation and progressive nature of CF, across both pulmonary and extrapulmonary systems. Access to CFTR modulator therapies in people with CF is occurring in a staged, descending age process, with clinical trials focusing primarily on safety and efficacy. There is a lack of robust, real-world longitudinal data on CFTR modulator therapy in infants and young children where extrapulmonary outcomes such as growth, micronutrient status and pancreatic function are the key focus.

Pancreatic, nutritional and clinical outcomes in children 0–5 years with CF during the first 2 years of CFTR modulator therapy (PaNC) is a prospective cohort study involving all eight tertiary paediatric CF centres in Australia. Infants and children 4 months to 5 years of age who are eligible for elexacaftor/tezacaftor/ivacaftor (ETI) or ivacaftor (IVA) meet the inclusion criteria for PaNC, with a total eligible cohort of 303 children at the commencement of recruitment. The primary outcomes are change in weight-for-length/body mass index z score and change in serum micronutrient status, at 6–12 monthly intervals, during the first 2 years of treatment with ETI or IVA. Secondary outcomes include change in exocrine pancreatic function, measured by faecal elastase-1, change in the use and dose of pancreatic enzyme replacement therapy, nutritional and gastrointestinal therapies and change in sweat chloride levels. Linear mixed modelling will be used to analyse primary and secondary endpoints. This protocol is reported in accordance with ‘The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement’ reporting guidelines.

Overarching governance and ethics approval has been granted by Monash Health Human Research Ethics Committee, in addition to all eight sites receiving site-specific authorisation approvals prior to the commencement of recruitment. Opportunities for CF consumers to be involved in targeted dissemination plans will be initiated via CF Australia at the completion of the study period. Additionally, a summary of non-identifiable results will be provided to CF consumers and CF healthcare providers via scientific and lay conferences and via peer-reviewed journals.

ACTRN12624001185550; Pre-results.