Analyses of Injury Count Data: Some Do's and Don'ts
Bibliographic record
Abstract
The analysis of injury data requires different considerations from the analysis of other types of outcomes because an individual can experience the outcome many times. When describing injury patterns using numerator-only data (e.g., proportion of upper-extremity injuries vs. lower-extremity injuries), simple comparisons of proportions are inappropriate because 1) individuals are compared with themselves and 2) multiple testing increases the potential for incorrect inference. Bootstrapping (resampling) techniques can be used to determine confidence intervals and whether the frequencies significantly differ across categories. When describing injury rates, the authors suggest plotting the observed injury rate against the number of exposures to obtain a visual representation of the heterogeneity of risk across individuals. Because the distribution of injury rates is often skewed, some research questions may be best addressed by comparing the weighted median injury rates instead of the weighted mean injury rates (which are given by standard formulae). Again, resampling techniques can be used to obtain a null distribution for injury rates in order to determine whether there are subjects who have unexpectedly high injury rates. More advanced analyses are required to account for multiplicity.
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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 | 0.343 | 0.506 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.003 | 0.026 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".