Epidemiology of injuries in elite male and female futsal: a systematic review and meta-analysis
Bibliographic record
Abstract
The main purpose of this study was to conduct a systematic review and meta-analysis quantifying the incidence of injuries in futsal players. A systematic search was conducted using MEDLINE, PubMed, Web of Science and Scopus databases and subsequently, six studies (14 cohorts) were selected. Separate meta-analyses for male and female players were conducted using a Poisson random-effect regression model approach. The overall and match incidence rates in elite male futsal players were 6.8 (95% CI = 0.0-15.2) and 44.9 (95% CI = 17.2-72.6) injuries/1000 hours of exposure. Pooled training injury rate in male players was not calculated due to the lack of studies reporting training injuries in this cohort. For females, an overall, training and match incidence rates of 5.3 (95% CI = 3.5-7), 5.1 (95% CI = 2.7-7.6) and 10.3 (95% CI = 0.6-20.1) injuries/1000 hours of exposure were reported. In males, match incidence rate in International tournaments was 8.5 times higher than in national leagues (77.2 [95% CI = 60.0-94.5] vs 9.1 [95% CI = 0.0-19.3] for international tournaments and national leagues, respectively). Elite male and female futsal players are exposed to a substantial risk of sustaining injuries, especially during matches.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.033 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".