Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 6 appraisals
PloS one
Analysis of swimming teaching programs, aquatic competencies and specific skills in school settings from early childhood to secondary education: A systematic review.
The teaching of swimming in school contexts has become increasingly important due to the benefits in the integral development of students and the prevention of aquatic accidents.This study analyses, by means of a systematic review, the aquatic teaching programmes and Competencies implemented in school settings over the last 25 years, from infant to secondary education. The Methodological approach was based on the PRISMA guidelines, with searches in scientific databases (PubMed, Scopus, Sport Discus and Web of Science) and secondary sources. Sixteen studies were selected according to the PICOS model, assessed with the STROBE and TREND tools. The results show a predominance of technical-utilitarian approaches focused on specific swimming skills, with little curricular integration and limited involvement of PE teachers. Most of the studies focused on infant and primary school, being scarce in secondary school. A high variability in duration, frequency, Contents and Assessment instruments was evidenced. The studies highlight the need for greater inclusion of swimming in school curricula, with more comprehensive pedagogical approaches adapted to each stage. It is concluded that although progress has been made, systematic implementation is still limited, requiring greater institutional support, teacher training and methodological coherence.
25 July 2026
Read appraisal →Journal of epidemiology and community health
Role of socioeconomic status in modifying the relationship between alcohol use and injury events: evidence from the UK Biobank
BACKGROUND: Alcohol consumption is a well-established risk factor for injuries, yet the role of socioeconomic status (SES) in modifying this relationship remains underexplored. This study examines how SES influences the association between alcohol use and injury events, including both fatal and non-fatal injuries as well as the risk of repeated injuries requiring hospitalisation. METHODS: Using data from the UK Biobank, we applied Cox proportional hazards models to assess whether SES modifies the relationship between different dimensions of alcohol use, average level and heavy episodic drinking (HED), and injury events. For injuries requiring hospitalisation, we fitted negative binomial regression models to estimate incidence rate ratios (IRRs) for the total number of unique injury admissions per individual. RESULTS: Among 482 078 participants, 48 045 injuries were recorded during follow-up. A statistically significant interaction was observed between alcohol use and low education, with individuals in lower education groups showing elevated injury risk compared with the high education group, particularly among those consuming 20-40 g/day with HED (HR: 1.17, 95% CI 1.06 to 1.28) and those consuming over 40 g/day (HR: 1.13, 95% CI 1.04 to 1.23). For non-fatal injuries, similar statistically significant interactions were found (IRR 1.17, 95% CI 1.06 to 1.28; and 1.18, 95% CI 1.08 to 1.29, respectively). CONCLUSION: Our findings highlight that individuals with lower SES face higher risks of alcohol-related injuries. Future research should investigate specific mechanisms underlying these disparities to inform more targeted policies. Population-based pricing policies could be combined with community-based programmes and tailored health messaging to address SES-related vulnerabilities.
13 June 2026
Read appraisal →Injury prevention : journal of the International Society for Child and Adolescent Injury Prevention
Injuries from falling out of bed in infants under 1 year of age: a systematic review
CONTEXT: Falls from beds are a leading cause of unintentional injuries in infants under 1 year of age. While most cases result in mild outcomes, the potential for severe injuries, such as skull fractures or intracranial haemorrhages, exists. Despite the prevalence of these incidents, no systematic review has focused specifically on injuries from bed falls in this specific age. OBJECTIVE: To evaluate the risk and severity of injuries caused by falls from beds in infants under 1 year of age through a systematic review. DATA SOURCES: We searched in PubMed, Embase, Cochrane Library and Lilacs Scielo for studies published between 1984 and December 2024. STUDY SELECTION: We included observational studies that reported injuries from falls from beds in infants under 1 year. DATA EXTRACTION: We extracted the relevant data from the included study details, recruitment setting, study design, sample size and outcome measures. We conducted a quality assessment using the Newcastle-Ottawa Scale. RESULTS: Out of 297 initially identified studies, three met inclusion criteria, comprising a total of 2034 infants under 1 year of age. Most falls resulted in minor or no injuries. Warrington et al reported <1% of cases leading to concussion or fractures. Samuel et al identified two cases of intracranial haemorrhage and linear skull fractures without intervention. Kolulu et al observed significant injuries, including skull fractures and cerebral contusions, in 10.2% of infants. CONCLUSIONS: Falls from beds are frequent in infants under 1 year, with most resulting in minor outcomes. However, serious injuries occur in around 5% of infants below 1-year old, emphasising the need for prevention strategies and clinical guidelines for managing post-fall assessments. Further research is required to refine understanding and improve prevention and management practices. PROSPERO REGISTRATION NUMBER: CRD42024626255.
17 May 2026
Read appraisal →Injury prevention : journal of the International Society for Child and Adolescent Injury Prevention
Large language models in public health: opportunity or threat? The case of button battery injuries.
BACKGROUND: Button battery (BB) injuries in children represent a severe and growing public health burden. The literature on the topic is extensive; however, there is a notable lack of structured public health initiatives addressing the problem. The present study aimed to test the feasibility of using large language models (LLMs) to draft recommendations for preventing and managing BB ingestion in children. METHODS: A set of questions was generated and submitted to ChatGPT-4o and ChatGPT-o1-preview. Questions were based on statements and websites of scientific societies and not-for-profit organisations and were developed to produce comprehensive recommendations that provided information on BB risks, primary and secondary prevention, clinical management and follow-up, and general public health initiatives. Two independent reviewers rated the accuracy and readability of the questions submitted to the LLMs. The accuracy was rated using a four-level scale, while the readability was assessed using two established readability tools, the Flesch Reading Ease (FRE) and the Flesch-Kincaid Grade Level (FKGL). RESULTS: None of the answers provided by the LLMs were rated as completely incorrect or partially incorrect. ChatGPT-o1-preview outperformed ChatGPT-4o in accuracy, with eight answers rated as accurate and complete. Both models showed similar readability levels, with high FKGL and FRE scores indicating college-level comprehension. DISCUSSION: LLM demonstrated a strong performance in this study, with no responses rated as incorrect or partially incorrect, showing its great potential and feasibility for use in public health. CONCLUSIONS: The present findings suggested the potential feasibility of LLMs in public health for preventing paediatric injuries.
17 May 2026
Read appraisal →Accident; analysis and prevention
Which approach better samples extreme traffic conflicts? Conventional- vs. machine learning-based sampling methods.
Extreme value theory has been receiving much attention of late for proactively estimating crash risk through a two-step procedure that first samples extreme traffic conflicts and then estimates crash risk based on those sampled extremes. Although the existing body of research has encapsulated sampling methods within a predominant conventional technique, there is no universally accepted practice on how to efficiently select threshold values, nor on how to evaluate the sampled extreme conflicts alignment with the conceptual crash severity level framework. This research aims to address these issues by employing machine learning-based sampling methods, which do not require predefined thresholds, and by comparing the sampled extremes with the conceptual severity levels, to assess their alignment. After a review of recent developments in machine learning techniques in transportation and other engineering fields, two promising machine learning sampling models, autoencoder neural network and isolation forest, were investigated using a database of vehicle-to-pedestrian conflicts at urban signalized intersections. Sampled extreme conflicts using the machine learning and conventional sampling techniques-as a baseline -were assessed and compared using two criteria: their visual alignment with the conceptual severity level framework, and their compatibility with the extreme value distribution. The results demonstrate that the extreme conflicts selected based on the machine learning methods better mirror the conceptual severity levels than the conventional sampling technique. Moreover, extremes classified by the isolation forest more closely preserve the characteristics of the empirical tail distributions, demonstrating a better contextual representation for modeling with the extreme value distribution compared to the autoencoder neural network and conventional sampling methods.
9 May 2026
Read appraisal →PloS one
Prediction of on-field rugby scrummaging contact forces from videos using artificial neural networks
Rugby is a contact sport with a high risk of injury. Scrums are a key component of the game and represent a collision event associated with serious injuries, especially to the cervical spine. Traditional methods for measuring scrum forces rely on both pressure sensors and scrum machines, but these have limitations for on-field monitoring. Thus, this study investigates the feasibility of using a machine learning approach to predict contact forces in rugby scrummaging during real-game situations, based on only anatomical landmarks of players extracted from video footage. A recurrent neural network (RNN) model was trained to predict contact forces from velocity signals. A dataset of eleven rugby teams (22 forward packs, n = 176 players) during on-field scrum engagements was used. Data augmentation techniques using a generative adversarial network and the Mixup technique were applied to increase the size of the training dataset. The RNN was trained using time-dependent data, with 2D trajectories from video landmarks as inputs and force signals from pressure sensors as outputs. The RNN´s prediction results showed good to excellent agreement between the predicted and measured time-dependent normal contact force signals, with a mean correlation of 0.95 ± 0.05. The mean normalized RMSE was 9.3 ± 4.3% and the mean normalized absolute difference in peak force was 6.7 ± 5.5%. This study demonstrated the feasibility of using RNNs to quantify contact forces in rugby scrummaging using only single-camera video, without players wearing sensors. In combination with a 2D human pose estimation model, the ANN trained in this study could support performance analysis, coaching, technique correction, and injury prediction using in-game data.
9 Apr 2026
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