Subsequent Injury Definition, Classification, and Consequence
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it