Rates and Risks of Injury during Intercollegiate Basketball
Bibliographic record
Abstract
BACKGROUND: Previous studies of basketball injury have not been able to assess injury incidence and risk. PURPOSE: To determine rates and risks of injury in Canadian intercollegiate basketball. STUDY DESIGN: Prospective cohort study. METHODS: Standardized data were collected with a validated instrument from 98.1% of the 318 athletes on the eight men's basketball teams in the Canada West Division of the Canadian Intercollegiate Athletic Union. RESULTS: A total of 142 athletes sustained 215 injuries (44.7% of players injured) over the 2-year study period. The greatest number of injuries resulting in more than seven sessions of time loss involved the knee, whereas the most common injuries causing fewer than seven sessions of time loss involved the ankle. The most common mechanism of injury was contact with another player, especially in the "key." Injuries occurred 3.7 times more often in games than during practice. Centers had the highest rate of injury, followed by guards, and then forwards. The relative risk of reinjury was significantly increased by previous injuries to the elbow, shoulder, knee, hand, lower spine or pelvis, and by concussions. CONCLUSIONS: Risk factors for injury were previous injury, games as opposed to practice, player position, player contact, and court location.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".