Injury and illness in short-course triathletes: A systematic review
Bibliographic record
Abstract
BACKGROUND: Determining the incidence and prevalence of injury and illness in short-course triathletes would improve understanding of their etiologies and therefore assist in the development and implementation of prevention strategies. This study synthesizes the existing evidence on the incidence and prevalence of injury and illness and summarizes reported injury or illness etiology and risk factors affecting short-course triathletes. METHODS: This review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Studies reporting health problems (injury and illness) in triathletes (all sexes, ages, and experience levels) training and/or competing in short-course distances were included. Six electronic databases (Cochrane Central Register of Controlled Trials, MEDLINE, Embase, APA PsychINFO, Web of Science Core Collection, and SPORTDiscus) were searched. Risk of bias was independently assessed by 2 reviewers using the Newcastle-Ottawa Quality Assessment Scale. Two authors independently completed data extraction. RESULTS: The search yielded 7998 studies, with 42 studies eligible for inclusion. Twenty-three studies investigated injuries, 24 studies investigated illnesses, and 5 studies investigated both injuries and illnesses. The injury incidence rate ranged 15.7-24.3 per 1000 athlete exposures, and the illness incidence rate ranged 1.8-13.1 per 1000 athlete days. Injury and illness prevalence ranged between 2%-15% and 6%-84%, respectively. Most injuries reported occurred during running (45%-92%), and the most frequently reported illnesses affected the gastrointestinal (7%-70%), cardiovascular (14%-59%), and respiratory systems (5%-60%). CONCLUSION: The most frequently reported health problems in short-course triathletes were: overuse and lower limb injuries associated with running; gastrointestinal illnesses and altered cardiac function, primarily attributable to environmental factors; and respiratory illness mostly caused by infection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.048 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".