First concussion did not increase the risk of subsequent concussion when patients were managed appropriately
Bibliographic record
Abstract
Some studies suggest that having a concussion during athletic activities increases the risk (as expressed through ‘rate ratios’) of subsequent concussion,1 potentially because of incomplete recovery after brain trauma. However, these analyses are flawed for causal interpretation because participants with one concussion likely have different inherent risks compared with those with no concussions.2 The issue is clinically important because patients may decide to return to their sport if their risk of concussion is simply due to the body type, sport and their personal style of play (ie, unchanged compared with prior to the injury) versus being at twice or thrice the risk of the first concussion because the concussion has caused permanent damage. To address whether a first concussion causally increases the risk of subsequent concussion, we analysed data from Cirque du Soleil (CdS) artists who had two or more concussions.2 3 We provide methodological details in the online supplementary appendix. The analysis in this paper is a matched analysis comparing risk of first concussion to risk of second concussion in the same individuals, thus controlling for different inherent risks similar to case-cross-over designs.4 These matched strategies assume that the increased risk for a …
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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 | 0.011 | 0.094 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".