Adding confidence to our injury burden estimates: is bootstrapping the solution?
Bibliographic record
Abstract
Injury burden is a composite measure of injury incidence and mean severity that can be used to understand the overall impact of injuries and help identify priority areas for injury prevention. Injury burden has been used within rugby union epidemiological studies since the early 2000s, but it is now recognised and recommended within other sports, including the most recent International Olympic Committee consensus statement for the recording and reporting of epidemiological data on injury and illness. Injury burden is normally reported as athlete days absence per 1000 athletehours and is derived from the product \nof injury incidence (expressed as injuries sustained/1000 athlete-hours) and severity (expressed as the mean severity of injury in days). \n \nWhile the value of injury burden as an output measure from injury surveillance studies is evident, there appears to be some confusion in the literature regarding its calculation. For instance, some authors \nhave used median severity to calculate injury burden rather than mean \nseverity, as discussed in a recent critical review. In addition, there appears to be no clear guidance within the sports medicine literature regarding the most appropriate way to calculate confidence intervals (CIs) for this metric.
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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 | 0.094 | 0.504 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.021 | 0.039 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".