Past Injury as a Risk Factor: An Illustrative Example Where Appearances Are Deceiving
Bibliographic record
Abstract
Previous injury is believed to be a causal risk factor for subsequent injury. Using empirical data on circus artists (n = 1,281 artists) between 2004 and 2008 in Montreal, Canada, as a motivating example, the authors use patient vector plots to demonstrate that a bias away from the null must always occur in the typical analyses cited as evidence (i.e., survival analysis, Poisson regression), except in the improbable context where all subjects have the same inherent risk independent of previous injury. In addition, using simulated data, the authors demonstrate that a simple method that conditions on the individual will approximate conclusions from more complex analytical methods. By using the typical analysis of the authors' empirical data, Kaplan-Meier curves and Cox regression suggested increasing injury rates for both the second and third injuries compared with the first injury. However, conditional analyses using a matched population (i.e., time to first, second, and third injuries among artists with 3 or more injuries) showed that injury rates were unchanged for both the second and third injuries compared with the first injury. These results suggest that previous injury should not be evaluated as a causal risk factor unless one conditions on the individual in some way.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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; 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".