Epidemiology of all‐complaint injuries in youth basketball
Bibliographic record
Abstract
This study evaluated the incidence and characteristics of all-complaint injuries, including acute and overuse injuries, in female and male youth basketball players. A total of 518 players (16 ± 1.4 years; 38.6% females), from 63 teams, participated in this prospective cohort study. Players were observed through one competitive high school or club basketball season to record exposure and all-complaint injuries, defined as any complaint resulting from participating in basketball-related activities, including but irrespective of the need for medical attention or time loss. Injury incidence rates and rate ratios were derived from Poisson's regression with 99.4% CI (Bonferroni's correction for multiple comparisons). The overall injury incidence rate was 14.4 (99.4% CI: 12.2-17.0) injuries/1000 h; 13.8 (99.4% CI: 11.2-16.8) in females and 14.8 (99.4% CI: 11.7-18.8) in males. While the incidence of injury was similar across injury classifications for female and male players, a potential lower overuse knee injury rate was noted for females vs males [IRR = 0.61 (99.4% CI: 0.34-1.07)]. The most commonly injured body location was the ankle (45%) in females and the knee (51%) in males. Overuse (vs acute) injuries were about 2x more common in the knee while acute (vs overuse) injuries were about 3x more common in the ankle, overall, and for female and male players. Based on an all-complaint injury definition, injury rates in competitive female and male youth basketball players are much higher than previously reported. This study provides an evidence base to inform more tailored interventions to reduce injuries in youth basketball.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".