Cardiovascular diseases (CVDs) are the leading cause of death worldwide, making the development of self-management strategies crucial for preventing complications and improving clinical outcomes. This process involves symptom monitoring, treatment adherence, emotional management and a healthy lifestyle, among others. Reliable instruments are necessary to measure self-management, requiring robust psychometric properties. In this way, this COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN)-based systematic review aims to assess the quality of specific self-management instruments for adults with CVDs.

This systematic review will follow the COSMIN and be reported according to the Preferred Reporting Items for Systematic Review and Meta-analysis Protocol. Searches will be conducted in seven databases: MEDLINE, Web of Science, Scopus, PsycINFO, EMBASE and CINAHL. Additionally, a manual search will be performed on PROQOLID, PROMIS and The Medical Outcome Trust websites. Studies on the development and validation of patient-reported instruments measuring specific self-management for individuals with CVDs will be included, without language or date restrictions. The search will be performed in November 2025, with the final version of the review expected to be completed in October 2026. Data extraction will follow COSMIN recommendations. The Modified Grading of Recommendations, Assessment, Development and Evaluation approach will be used to determine the quality of evidence. Instruments will be categorised according to COSMIN recommendations. All steps will be conducted by two independent reviewers, with a third reviewer involved in case of discrepancies. Additionally, the content of the instruments will be analysed and linked to the International Classification of Functioning, Disability and Health, following international recommendations.

This study does not require ethics committee approval as it is a review of published data. The review results will be disseminated through peer-reviewed journal publications and presentations at scientific conferences.

CRD42024605969.

Cisplatin is a widely used chemotherapeutic anti-cancer drug. However, high-dose cisplatin is also known for its dose-limiting toxicities, including irreversible cisplatin-induced hearing loss (CIHL). Sodium thiosulphate (STS) can bind to cisplatin to form an inactive and harmless complex. A topical application is desired, allowing cisplatin to retain its systemic anti-cancer effect.

The SOUND trial is an investigator-initiated randomised controlled multicentre phase III trial to study the efficacy of transtympanic administration of STS against CIHL in a cohort of 100 patients with head and neck cancer treated with cisplatin at a dose of ≥200 mg/m². Each subject will receive transtympanic STS injections in one ear, chosen by randomisation, before each cisplatin infusion. The contralateral ear serves as an internal control. The primary objective is efficacy (ie, clinically relevant benefit) of transtympanic STS injections against CIHL, defined as a difference in threshold shift of ≥10 decibels between baseline and 3 months after treatment in favour of the STS-treated ear. Secondary objectives include the difference in mean threshold shifts on frequencies essential for speech and extended high frequencies, as well as the difference between both ears in the gradation of hearing loss as defined by ototoxicity grading scales.

The medical ethics committee in the Netherlands approved the trial (Clinical Trials Information System (CTIS) 2023-503313-30-00). The results will be disseminated through the CTIS and peer-reviewed scientific journals.

CTIS 2023-503313-30-00 approved by Medical Research Ethics Committee NedMec.

Depressed mood is a psychological state characterised by sadness or loss of interest in activities, is a common symptom that accompanies most major mental disorders. It is therefore reasonable to consider it as a transdiagnostic target, which when addressed, may improve the functioning and quality of life of persons with lived experience of mental disorders. However, there is limited understanding of the depressed mood as a transdiagnostic target across major mental disorders. Therefore, this scoping review aims to synthesise knowledge on depressed mood, its measurement and interventions among persons with anxiety and/or psychosis.

This scoping review followed Arksey and O'Malley’s framework. Peer-reviewed articles and grey literature published from January 1988 to April 2024 were searched in the following databases: Medline/PubMed, Scopus, Web of Science, Africa-Wide Information, Cumulative Index to Nursing and Allied Health Literature, PsycINFO, SocINDEX, Humanities International Complete, Sabinet, Open Grey and Google Scholar. Articles were screened at title, abstract and full article levels. Data extracted were analysed using thematic analysis and reported following Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews guidelines. We also consulted stakeholders such as lived experience experts, clinicians and researchers to contextualise our findings.

We screened 245 full articles out of the 4039 hits and included 28 articles in this review. Although depressed mood is conceptually different from clinical depression, the terms are used interchangeably in the literature. The prevalence of depressed mood in psychosis was 7.3–33.3%, with no prevalence studies specific to anxiety disorders. Commonly used outcome measures included Beck’s Depression Inventory (n=6) and Patient Health Questionnaire-9 (n=5). Psychosocial conservative interventions such as cognitive–behavioural therapy were the most common interventions. Other interventions, including yoga, pharmacotherapy and Ecology Momentary Interventions, were also reported. All interventions were reported to improve depressed mood, and most were implemented in high-income settings. Stakeholders, including lived experience experts, concurred on the importance of using depressed mood as a transdiagnostic target, viewing it as a ‘window’ for early identification and management of many common mental disorders.

There is a need to clarify the definition and diagnostic cut-off points on common outcome measures of depressed mood. There is also a need for increased research on depressed mood as a viable transdiagnostic target in anxiety and/or psychosis with a special focus on low-to-middle income countries.

Depressed mood is an important and prevalent transdiagnostic target with great promise for early management in anxiety and/or psychosis. Valid diagnostic and measurement tools are developing, and so are the targeted interventions in the context of anxiety and/or psychosis.

To assess family caregiver burden and associated factors among caregivers of people with mental illness in Eastern Ethiopia.

Cross-sectional study.

Three hospitals in Eastern Ethiopia.

A total of 422 family caregivers of people with mental illness were recruited using systematic random sampling, of whom 417 participated (response rate 98.8%).

Caregiver burden was assessed using the Zarit Burden Interview (ZBI-22). Linear regression was used to measure the associations between dependent and independent variables.

The mean score of the ZBI was 47.971 (SD=14.539). In our study, factors associated with caregiver burden included age of caregivers (β=0.143, p=0.006), sex of the caregiver (β=0.121, p=0.007), time spent in providing care (β=0.194, p=0.006), presence of comorbid medical illness (β=0.309, p0.001), substance use in the last 12 months (β=0.265, p=0.024), perceived stigma (β=0.207, p

89 (22.2%) of caregivers reported severe burden and 220 (52.8%) reported moderate-to-severe burden. Family caregivers play a critical role in the treatment of mental illness; there is a need to establish family caregiver support services, such as group therapy and psychoeducation.

Many patients who are extubated after receiving mechanical ventilation for acute respiratory failure experience extubation failure (ie, require reintubation hours to days after extubation). High-quality evidence shows that extubating patients directly to non-invasive ventilation (NIV) or high-flow nasal cannula oxygen (HFNC), rather than conventional low-flow oxygen, can prevent extubation failure. These guideline-recommended interventions, however, require care coordination involving multiple intensive care unit (ICU) team members and are infrequently used. Interprofessional education (IPE), which teaches members of multiple professions together, could effectively address this implementation gap in complex, team-based, critical care settings, particularly when paired with a customisable protocol.

This batched, stepped-wedge, cluster-randomised, type 2 hybrid effectiveness–implementation trial will test three hypotheses: (1) when compared with traditional online education (OE), IPE increases implementation of preventive postextubation respiratory support, (2) the benefits of IPE are increased when paired with a clinical protocol and (3) preventive postextubation NIV for high-risk patients and preventive postextubation HFNC for low-risk patients reduce in-hospital mortality when compared with conventional postextubation oxygen therapy. The trial will recruit 24 clusters made up of one or more ICUs that care for at least 100 mechanically ventilated patients per year in a large multihospital health system in the USA. All clusters will receive OE, IPE and a clinical protocol, with timing determined by randomisation. We will also randomise half of the clusters to education promoting postextubation NIV for patients at high risk of extubation failure and preventive, postextubation HFNC for patients at lower risk, whereas the other half will be randomised to education promoting postextubation HFNC for all eligible patients. We will include all patients who are invasively mechanically ventilated for at least 24 hours. The primary implementation endpoint is the rate of use of postextubation NIV or HFNC among eligible participants. The primary clinical endpoint is in-hospital mortality truncated at 60 days from intubation.

This study was approved by the institutional review board of the University of Pittsburgh and an independent data safety monitoring board. We describe the methods herein using the Standard Protocol Items for Randomised Trials framework and discuss key design decisions. We will disseminate results to participating healthcare providers, through publication in a peer-reviewed medical journal and via presentations at international conferences.

NCT05523479.

The aim of this study was to prioritise a set of indicators to measure World Health Organization (WHO) quality-of-care standards for small and/or sick newborns (SSNB) in health facilities. The hypothesis is that monitoring prioritised indicators can support accountability mechanisms, assess and drive progress, and compare performance in quality-of-care (QoC) at subnational levels.

Prospective, iterative, deductive, stepwise process to prioritise a list of QoC indicators organised around the WHO Standards for improving the QoC for small and sick newborns in health facilities. A technical working group (TWG) used an iterative four-step deductive process: (1) articulation of conceptual framework and method for indicator development; (2) comprehensive review of existing global SSNB-relevant indicators; (3) development of indicator selection criteria; and (4) selection of indicators through consultations with a wide range of stakeholders at country, regional and global levels.

The indicators are prioritised for inpatient newborn care (typically called level 2 and 3 care) in high mortality/morbidity settings, where most preventable poor neonatal outcomes occur.

The TWG included 24 technical experts and leaders in SSNB QoC programming selected by WHO. Global perspectives were synthesised from an online survey of 172 respondents who represented different countries and levels of the health system, and a wide range of perspectives, including ministries of health, research institutions, technical and implementing partners, health workers and independent experts.

The 30 prioritised SSNB QoC indicators include 27 with metadata and 3 requiring further development; together, they cover all eight standard domains of the WHO quality framework. Among the established indicators, 10 were adopted from existing indicators and 17 adapted. The list contains a balance of indicators measuring inputs (n=6), processes (n=12) and outcome/impact (n=9).

The prioritised SSNB QoC indicators can be used at health facility, subnational and national levels, depending on the maturity of a country’s health information system. Their use in implementation, research and evaluation across diverse contexts has the potential to help drive action to improve quality of SSNB care. WHO and others could use this list for further prioritisation of a core set.

Progress at the intersection of artificial intelligence and paediatric neuroimaging necessitates large, heterogeneous datasets to generate robust and generalisable models. Retrospective analysis of clinical brain MRI scans offers a promising avenue to augment prospective research datasets, leveraging the extensive repositories of scans routinely acquired by hospital systems in the course of clinical care. Here, we present a systematic protocol for identifying ‘scans with limited imaging pathology’ through machine-assisted manual review of radiology reports.

The protocol employs a standardised grading scheme developed with expert neuroradiologists and implemented by non-clinician graders. Categorising scans based on the presence or absence of significant pathology and image quality concerns facilitates the repurposing of clinical brain MRI data for brain research. Such an approach has the potential to harness vast clinical imaging archives—exemplified by over 250 000 brain MRIs at the Children’s Hospital of Philadelphia—to address demographic biases in research participation, to increase sample size and to improve replicability in neurodevelopmental imaging research. Ultimately, this protocol aims to enable scalable, reliable identification of clinical control brain MRIs, supporting large-scale, generalisable neuroimaging studies of typical brain development and neurogenetic conditions.

Studies using datasets generated from this protocol will be disseminated in peer-reviewed journals and at academic conferences.

Adolescence is a critical period marked by rapid brain development and the onset of many mental health disorders. Brain MRI studies during adolescence, especially when paired with behavioural phenotypes and information about genetic risk factors, hold promise to advance early identification of mental health risk and spur the creation of targeted treatments to improve patient function, prognosis and quality of life. However, prospective neuroimaging is costly and time-intensive, and individuals who participate may not be reflective of the general population. These challenges are compounded when examining adolescents, as many families lack the time, energy or resources to participate in studies that use research-grade imaging. Repurposing clinical MRIs obviates many of the challenges of neuroimaging research. Here, we describe the brain-behaviour-genetics study protocol. This protocol describes procedures used to recruit participants with recent high-quality clinical brain MRIs and prospectively acquire genetic and sociobehavioural data, resulting in a highly cost-efficient design that harnesses a vast and underused neuroscientific resource.

The brain-behaviour-genetics protocol aims to recruit 1000 adolescents who have clinical brain MRIs contained in Children’s Hospital of Philadelphia’s electronic health record. One or both parents of the adolescent proband will be recruited when possible. Parents and adolescents will complete a series of self-report scales spanning the domains of mental health, trauma, risk and resilience. Saliva samples will be collected from the adolescent and at least one biological parent, using an at-home saliva collection kit. Subsequent analysis will examine associations between brain development, genetics and behavioural measures in adolescence.

Approval for the study had been obtained from the Children’s Hospital of Philadelphia’s institutional review board (IRB #23–0 20 851). Results will be published in peer-reviewed journals.

To assess the time to first optimal glycaemic control and its predictors among adult patients with type 1 and type 2 diabetes at the University of Gondar Comprehensive Specialized Hospital in Ethiopia.

A retrospective cohort study.

University of Gondar Comprehensive Specialized Hospital, northwest, Ethiopia.

We recruited 423 adult diabetic patients who were diagnosed between 1 January 2018 and 30 December 2022 at the University of Gondar Comprehensive Specialized Hospital.

The primary outcome was the time from diagnosis to the achievement of the first optimal glycaemic control, measured in months. A Cox proportional hazards regression model was fitted to identify predictors of time to first optimal glycaemic control. Data were collected with KoboToolbox from patient medical charts and exported to Stata V.17. The log-rank test was used to determine the survival difference between subgroups of participants.

Median time to first optimal glycaemic control was 10.6 months. Among 423 adult diabetic patients, 301 (71.16%) achieved the first optimal glycaemic control during the study period. Age category (middle age (adjusted HR (AHR)=0.56, 95% CI 0.41 to 0.76), older age (AHR=0.52, 95% CI 0.33 to 0.82)), comorbidity (AHR=0.52, 95% CI 0.35 to 0.76), therapeutic inertia (AHR=0.20, 95% CI 0.13 to 0.30) and medication non-compliance (AHR=0.49, 95% CI 0.27 to 0.89) were significant predictors of time to optimal glycaemic control.

The median time to first optimal glycaemic control was prolonged. Diabetic care should focus on controlling the identified predictors to achieve optimal glycaemic control early after diagnosis.

Ethiopia, the second most populous country in Africa, faces significant demographic transitions, with fertility rates playing a central role in shaping economic and healthcare policies. Family planning programmes face challenges due to funding limitations. The recent suspension of the US Agency for International Development funding exacerbates these issues, highlighting the need for accurate birth forecasting to guide policy and resource allocation. This study applied time-series and advanced machine-learning models to forecast future birth trends in Ethiopia.

Secondary data from the Ethiopian Demographic and Health Survey from 2000 to 2019 were used. After data preprocessing steps, including data conversion, filtering, aggregation and transformation, stationarity was checked using the Augmented Dickey-Fuller (ADF) test. Time-series decomposition was then performed, followed by time-series splitting. Seven forecasting models, including Autoregressive Integrated Moving Average, Prophet, Generalised Linear Models with Elastic Net Regularisation (GLMNET), Random Forest and Prophet-XGBoost, were built and compared. The models’ performance was evaluated using key metrics such as root mean square error (RMSE), mean absolute error (MAE) and R-squared value.

GLMNET emerged as the best model, explaining 77% of the variance with an RMSE of 119.01. Prophet-XGBoost performed reasonably well but struggled to capture the full complexity of the data, with a lower R-squared value of 0.32 and an RMSE of 146.87. Forecasts were made for both average monthly births and average births per woman over a 10-year horizon (2025–2034). The forecast for average monthly births indicated a gradual decline over the projection period. Meanwhile, the average births per woman showed an increasing trend but fluctuated over time, influenced by demographic shifts such as changes in fertility preferences, age structure and migration patterns.

This study demonstrates the effectiveness of combining time-series models and machine learning, with GLMNET and Prophet XGBoost emerging as the most effective. While average monthly births are expected to decline due to demographic transitions and migration, the average births per woman will remain high, reflecting persistent fertility preferences within certain subpopulations. These findings underscore the need for policies addressing both population trends and sociocultural factors.