ASSESSING REMEDIES FOR MISSING WEEKLY INDIVIDUAL EXPOSURE IN SPORT INJURY STUDIES
Bibliographic record
Abstract
Background In sport injury epidemiology research, the injury incidence rate (IR) is defined as the number of injuries over a given length of participation time (exposure, e.g. game hours). However, it is common that individual weekly exposure is missing due to requirements of personnel at every game to record exposure information. Ignoring this issue will lead to an inflated injury rate because the total exposure serves as the denominator of IR, where the number of injury cases were captured accurately. Objective To compare 6 methods to handle missing weekly exposure of individual players. Design Data collected from a large community cohort study in youth ice hockey. Setting Youth ice hockey. Participants Pee Wee (age 11–12) ice hockey players. Interventions The 6 methods to handle missing weekly exposures include available case analysis, last-observation-carried-forward, mean imputation, multiple imputation, bootstrapping, and best/worst case analysis. Main outcome measures Injury rate ratios (IRR) between Alberta and Quebec, as in the original study, three statistical models were applied to the imputed datasets: Poisson, zero-inflated Poisson, and negative binomial regression models. Results The final sample for imputation included 2098 players for whom 12.5% of weekly game hours were missing. Estimated IRs and IRRs with confidence intervals from different imputation methods were similar when the proportion of missing was small. Simulations showed that mean and multiple imputations provide the least biased estimates of IRR when the proportion of missing was large. Conclusion Complicated methods like multiple imputation or bootstrap are not superior over the mean imputation, a much simpler method, in handling missing weekly exposure of injury data where weekly exposures were missing at random.
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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 | 0.519 | 0.814 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".