Estimating unbiased sports injury rates: a compendium of injury rates calculated by athlete exposure and athlete at risk methods
Bibliographic record
Abstract
A basic principle in epidemiology is that an injury rate should only include time when a person is ‘at risk’ for the outcome. However, when calculating injury rates in sports medicine, many investigators use a method known as ‘athlete- exposures’ (AE) which was originally proposed by the National Collegiate Athletic Association (NCAA) surveillance programme.1 The AE method overestimates game injury rates when compared with using individual player time as the AE method attributes a full exposure to those who do not play a full game.2 Another method of capturing player exposure to injury is called the athletes-at-risk (AAR) method.2 The AAR method follows proper epidemiological principles, provides results very similar to the individual player time method in most contexts, and is easier to calculate. While the AE method will most often underestimate injury rates, the amount of underestimation depends on the sport and context. Our previous publication2 discusses these concepts …
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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 | 0.034 | 0.155 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 0.009 |
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".