Strengthening research capacity in Africa is vital for tackling pressing health, educational and socioeconomic challenges facing the continent. At the core of this effort is the cultivation of innovative research leaders through postgraduate training programmes that incorporate mentorship-infused supervision. Such models have demonstrated potential in improving research skills, boosting academic productivity and fostering leadership development among emerging scholars. This systematic review and meta-synthesis protocol aims to examine existing mentorship-infused supervision practices across African higher education institutions. The review seeks to identify effective models, uncover common challenges and barriers, and generate evidence-based recommendations to develop sustainable, contextually relevant strategies. Insights from this work will inform policies and practices to enhance postgraduate research training, advance research leadership and contribute to the broader goal of strengthening research ecosystems across Africa.

A systematic review and thematic meta-synthesis will be undertaken, focusing on qualitative research studies as well as the qualitative components of mixed-methods studies. Relevant studies published in English will be identified through a comprehensive search strategy. The electronic databases, including Medline/PubMed, Scopus, Web of Science, African Journals Online, EMBASE and CINAHL, will be searched to capture a wide range of peer-reviewed articles and grey literature. Databases will be searched from March 2026. Two reviewers will independently perform study selection, data extraction, quality assessment and evaluation of risk of bias, using the Critical Appraisal Skills Programme checklist.

This systematic review and meta-synthesis will analyse publicly available literature and does not require ethical approval, as it involves no primary data collection. It will adhere to established ethical and methodological standards, including proper citation and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The findings will be widely disseminated through open-access journal publication, conference presentations and targeted reports for universities, research institutions and policymakers to inform and support mentorship-based postgraduate research supervision across Africa.

CDR420251049878. Available from https://www.crd.york.ac.uk/PROSPERO/view/CRD420251049878.

The ‘time-limited trial’ for patients with critical illness is a collaborative plan made by clinicians, patients and families to use life-sustaining therapies for a defined duration. After this period, the patient’s response to therapy informs decisions about continuing recovery-focused care or transitioning to comfort-focused care. The promise of time-limited trials to help navigate the uncertain limits and benefits of life-sustaining therapies has been extensively discussed in the palliative and critical care literature, leading to their dissemination into clinical practice. However, we have little evidence to guide clinicians in how to conduct time-limited trials, leading to substantial variation in how and why they are currently used. The overall purpose of this study is to characterise the features of an optimal time-limited trial through a rich understanding of how they are currently shaping critical care delivery.

We are conducting an observational, multicentre, focused ethnography of time-limited trials in patients with acute respiratory failure receiving invasive mechanical ventilation in six intensive care units (ICUs) within five hospitals across the US. Study participants include patients, their surrogate decision makers and ICU clinicians. We are pursuing two complementary analyses of this rich data set using the open-ended, inductive approach of constructivist grounded theory and, in parallel, the structured, deductive methods of systems engineering. This cross-disciplinary, tailored approach intentionally preserves the tension between time-limited trials’ conceptual formulation and their heterogeneous, real-world use.

This study has been reviewed and approved by the University of Wisconsin Institutional Review Board (IRB) as the single IRB (ID: 2022-1681; initial approval date 23 January 2023). Our findings will be disseminated through peer-reviewed publication, conference presentations, and summaries for the public.

NCT06042621.

Photobiomodulation (PBM) has shown promising effects in managing postoperative pain following conventional periapical surgery, although current evidence remains limited. This study aims to assess the effect of PBM on postoperative pain 24 hours after periapical surgery.

A randomised, controlled, double-blind trial will include 34 patients undergoing periapical surgery in the maxillary region, randomly assigned to an experimental group (n=17) or control group (n=17). The experimental group will receive PBM (GaAlAs diode laser, 808 nm, 100 mW, 4 J/cm², applied at five vestibular points) and placebo ibuprofen immediately and 24 hours postoperatively. The control group will receive simulated PBM and active ibuprofen. The primary outcome is postoperative pain assessed by the visual analogue scale at 24 hours. Secondary outcomes include pain at the seventh day, paracetamol intake, oedema, ecchymosis, soft tissue status and temperature at 24 hours and 7 days. Radiographic evaluation of healing will be performed at 1 and 3 months. Statistical analysis will be conducted based on data distribution, using repeated measures ANOVA (Analysis of Variance) or non-parametric equivalents for longitudinal outcomes, and appropriate tests for categorical variables. Significance will be set at p

The study was approved by the Human Research Ethics Committee of Universidad Católica del Uruguay (process no. 220914). Results will be disseminated to participants, healthcare professionals, the public and scientific communities.

NCT05935306.

There is limited evidence on how to effectively treat individuals from marginalised populations with dependence on amphetamine and/or methamphetamine (collectively referred to hereafter as amphetamine dependence). The disease burden is extremely high in this population, especially related to psychiatric comorbidities, cardiovascular complications, injection-related infections and poor social functioning. ATLAS4Dependence is a multi-centre randomised, placebo-controlled, double-blind trial that will investigate the effectiveness and safety of substitution treatment with dextroamphetamine compared with placebo in people with amphetamine dependence.

The trial will recruit 226 adult patients in several outpatient clinics in Norway.Inclusion criteria comprise individuals with amphetamine dependence, defined as use on three or more days per week during the past 28 days, who currently inject or have formerly injected drugs. This includes individuals both with and without comorbid opioid dependence, as well as those currently receiving or not receiving opioid agonist treatment. Participants will be randomly assigned 1:1 to receive either dextroamphetamine or placebo for 12 weeks. Flexible doses within the range of 30–120 mg daily will be provided based on individual assessments. The participants in both arms will be offered standard psychosocial and medical follow-up in accordance with current clinical practice. The endpoint assessments will be conducted at 12 weeks with weekly self-reports and safety assessments and a follow-up assessment at 52 weeks. The primary objective of the study is to assess the impact of 12 weeks daily prescribed oral dextroamphetamine versus placebo on the use of illicit amphetamines as well as on the total amount of amphetamines used (including both illicit and prescribed sources). Secondary outcomes are the differences between the groups at 12 weeks regarding psychological distress, symptoms of psychosis, quality of life, cardiovascular risk factors, injection-related infections, executive functioning, attention-deficit hyperactivity disorder-related symptoms, sleep, violence risk, fatigue, symptoms of craving and withdrawal, treatment retention, days of use of illicit amphetamines and use at 4 weeks and 8 weeks during the intervention period, use of other illicit substances and alcohol, as well as a cost-effectiveness analysis (using private economy, criminal activity and health service utilisation) and a qualitative approach to assess overall experiences with the study intervention. Analysis and reporting will follow the Consolidated Standards of Reporting Trials guidelines. All tests will be two-sided. Descriptive results and the estimated effectiveness will be presented with 95% CIs. The difference between the groups at the primary time point (at the end of the 12-week trial) will be assessed using ² test (for use of illicit amphetamines measured by monthly urine tests) and Analysis of Covariance (ANCOVA) (for weekly self-reported total amount of amphetamines). Analyses for the primary endpoint will be undertaken on an intention-to-treat basis and reported on as such, but sensitivity analyses with per protocol analyses will also be presented.

The study is approved by European Medicines Agency, Clinical Trial Information System (CTIS). Written informed consent will be obtained from all patients. Study results will be published in international peer-reviewed medical journals.

CTIS 2023-510404-44-00.

To assess the feasibility of conducting a definitive randomised controlled trial (RCT) to test the clinical and cost-effectiveness of a tailored exercise intervention compared with usual care for people aged 80 years and older with hip and/or knee osteoarthritis (OA) and comorbidities.

Two-arm, parallel-design, multicentre, pragmatic, feasibility RCT.

Four National Health Service outpatient physiotherapy services across England.

Adults aged 80 years and over with clinical hip and/or knee OA and ≥1 comorbidity.

Participants were randomised 1:1 via a central web-based system to be offered: (1) a 12-week tailored exercise programme or (2) usual care. Participants and outcome assessors were not blinded to treatment allocation.

(1) Ability to screen and recruit participants; (2) retention of participants at 14-week follow-up; (3) intervention fidelity (proportion of participants who received ≥4 intervention sessions as per protocol) and (4) participant engagement (assessed by home exercise adherence).

Between 12 May 2022 and 26 January 2023, 133 potential participants were screened, of whom 94 were eligible. The main reasons for ineligibility were symptoms not consistent with hip or knee OA (10/39, 25.6%) or already having had a physiotherapy appointment (8/39, 20.5%). 51 of 94 (54%) eligible participants were recruited. Participants had a mean age of 84 years (SD 3.5), 31 (60.8%) were female and 96.1% reported their ethnicity as White British (n=49/51). 45 of 51 participants (88%) provided outcome data at the 14-week follow-up time point. Four or more intervention sessions were attended by 13/25 (52%) participants. Home exercise log completion declined over time: 6/23 participants (26.1%) returned completed exercise logs for all 12 weeks. The median number of days home exercises were recorded each week was 5 (range 0–7).

This study demonstrated that a definitive trial would be feasible. Before proceeding, modifications to ensure recruitment of a diverse population and intervention fidelity should be addressed.

ISRCTN75983430.

Early childhood development (ECD) lays the foundation for lifelong health, academic success and social well-being, yet over 250 million children in low- and middle-income countries are at risk of not reaching their developmental potential. Traditional measures fail to fully capture the risks associated with a child’s development outcomes. Artificial intelligence techniques, particularly machine learning (ML), offer an innovative approach by analysing complex datasets to detect subtle developmental patterns.

To map the existing literature on the use of ML in ECD research, including its geographical distribution, to identify research gaps and inform future directions. The review focuses on applied ML techniques, data types, feature sets, outcomes, data splitting and validation strategies, model performance, model explainability, key themes, clinical relevance and reported limitations.

Scoping review using the Arksey and O‘Malley framework with enhancements by Levac et al.

A systematic search was conducted on 16 June 2024 across PubMed, Web of Science, IEEE Xplore and PsycINFO, supplemented by grey literature (OpenGrey) and reference hand-searching. No publication date limits were applied.

Included studies applied ML or its variants (eg, deep learning (DL), natural language processing) to developmental outcomes in children aged 0–8 years. Studies were in English and addressed cognitive, language, motor or social-emotional development. Excluded were studies focusing on robotics; neurodevelopmental disorders such as autism spectrum disorder, attention-deficit/hyperactivity disorder and communication disorders; disease or medical conditions; and review articles.

Three reviewers independently extracted data using a structured MS Excel template, covering study ML techniques, data types, feature sets, outcomes, outcome measures, data splitting and validation strategies, model performance, model explainability, key themes, clinical relevance and limitations. A narrative synthesis was conducted, supported by descriptive statistics and visualisations.

Of the 759 articles retrieved, 27 met the inclusion criteria. Most studies (78%) originated from high-income countries, with none from sub-Saharan Africa. Supervised ML classifiers (40.7%) and DL techniques (22.2%) were the most used approaches. Cognitive development was the most frequently targeted outcome (33.3%), often measured using the Bayley Scales of Infant and Toddler Development-III (33.3%). Data types varied, with image, video and sensor-based data being most prevalent. Key predictive features were grouped into six categories: brain features; anthropometric and clinical/biological markers; socio-demographic and environmental factors; medical history and nutritional indicators; linguistic and expressive features; and motor indicators. Most studies (74.1%) focused solely on prediction, with the majority conducting predictions at age 2 years and above. Only 41% of studies employed explainability methods, and validation strategies varied widely. Few studies (7.4%) conducted external validation, and only one had progressed to a clinical trial. Common limitations included small sample sizes, lack of external validation and imbalanced datasets.

There is growing interest in using ML for ECD research, but current research lacks geographical diversity, external validation, explainability and practical implementation. Future work should focus on developing inclusive, interpretable and externally validated models that are integrated into real-world implementation.