Pre-injury variables and risk of sport concussion
Bibliographic record
Abstract
Objective The study examined whether cognitive functioning, history of concussion (HOC), and sex predicted risk of sport concussion. Design Retrospective study design. Predictive models were used to determine the predictive ability of each variable. Setting Canadian University. Participants 708 data observations from 701 varsity athletes (41.2% female), representing 14 sports. Assessment of risk factors Two measures of cognitive functioning (mean reaction time and throughput [speed and accuracy]) were assessed using the Automated Neuropsychological Assessment Metrics testing battery. Categorical and continuous measures of HOC, and sex, were examined. Outcome measures Occurrence of concussion after baseline testing. Main results HOC was a significant predictor for both sexes. For every previous concussion, the odds of sustaining another concussion increased by 1.5 (95% Confidence Interval [CI]: 1.1, 2.1 [females]; 1.2, 1.9 [males]). Females with a HOC had twice the odds of sustaining another concussion than those without a HOC (CI: 1.1, 4.0). For males, the odds were three times (CI: 1.7, 5.6). Cognitive functioning and sex were not meaningful predictors. Conclusions This study supports previous findings that HOC is a risk factor and suggests that pre-injury cognitive functioning is not a risk factor for sport concussion. Assessing HOC in all athletes prior to their competitive season provides information regarding their risk of future sport concussion. Thus, it is important for clinicians to record HOC, and to encourage athletes to report concussions to ensure an accurate HOC record. Pre-injury cognitive screening of athletes is not recommended for assessing risk of future concussion. Competing interests Sandhya Mylabathula, Lynda Mainwaring, and Michael Hutchison None. Doug Richards is the Medical Director at the MacIntosh Sports Medicine Clinic at the University of Toronto Paul Comper is a clinical neuropsychologist consultant with the NHLPA and a member of the Concussion Working Group, but neither were related to the current research
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".