Les facteurs de risques de blessures à la main chez les boxeurs
Bibliographic record
Abstract
Background: Boxing places considerable demands on athletes, both professional and amateur, and exposes them to numerous injuries, particularly to the hand. Those injuries include carpo-metacarpal instability, boxer's knuckles and skier's thumb. Objective: to determine the risk factors for the development of hand injuries in boxers. Method: A search and analysis of studies on our topic was carried out over a period from 02/10/24 to 07/03/2025. This search was performed on the ScienceDirect, PubMed and Cochrane Library databases. After applying our inclusion and exclusion criteria, four articles were selected. These articles concerned boxers of all levels and ages. The Newcastle Ottawa Scale was used to assess the methodology of these articles and guarantee the reliability of the results. Results: Due to the difference in study design and hence the high degree of heterogeneity between the studies (two cohort studies and two cross-sectional studies), the results are difficult to compare. Nevertheless, a significant association between heavy bag training and hand injuries was established. Discussion: this literature review does not establish a causal link between certain risk factors and boxing. Although some associations have been identified, the level of evidence in the studies is insufficient to demonstrate a causal link. Further research with higher levels of evidence is needed to establish this relationship.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".