Subsequent Injury Definition, Classification, and Consequence
Bibliographic record
Abstract
OBJECTIVE: To examine if different definitions of "recurrent injury" affect the distribution of subsequent injury types and their consequences. DESIGN: Secondary analysis of prospective injury data. SETTING: Circus shows. PARTICIPANTS: Circus artists (n = 1281). MAIN OUTCOME MEASURES: A subsequent injury after an index injury was categorized as (1) new injury: different location; (2) local injury: same location, different type; and (3) recurrent injury: same location/type. Subsequent injuries were stratified according to when they occurred after the index injury: early (≤90 performances), late (91-540 performances), and delayed (>540 performances). "Healed injury" was either date of return to full participation (RTP) or last treatment. RESULTS: Eight hundred twenty-one artists (64%) incurred 2 medical attention injuries, and 296 artists (23%) incurred 2 time loss injuries. In both medical attention and time loss injuries, recurrent (range, 7.5%-8.3%) and local injuries (range, 4%-7%) occurred less frequently than subsequent new injuries (range, 81%-87%). Time loss injuries recurred later than medical attention injuries. The pattern of early, late, and delayed injuries was similar for new, local, and recurrent injuries. A greater number of "early" injuries are seen with the treatment definition compared with RTP. Subsequent injuries had similar number of treatments and missed performances (consequences) as index injuries. CONCLUSIONS: In our data, there were a greater number of local and recurrent time loss injuries compared with medical attention injuries, but the injury definition did not affect the relative number of early, late, or delayed injuries. Recurrent injuries are an important component of injury prevention, and clear definitions when presenting recurrent injury data are necessary.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".