Pre-injury variables and risk of sport concussion
Bibliographic record
Abstract
Introduction: Concussions are a public health concern in Canada, and may cause physiological and neuropsychological consequences. Research on risk factors is not extensive and many questions remain unanswered. Objective: This study examined whether cognitive functioning, history of concussion (HOC), and sex predicted risk of sport concussion. Design: Retrospective study design using logistic regression and predictive models. 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. Sex and self-reported HOC 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 provides sex-specific evidence that HOC is a risk factor and suggests that pre-injury cognitive functioning is not a risk factor for sport concussion. Thus, it is important for clinicians to record HOC, and to encourage athletes to report concussions to ensure accurate recording. Despite common practice, pre-injury cognitive screening of athletes is not recommended for assessing risk of future concussion.Acknowledgments: The researchers would like to acknowledge the participating University of Toronto varsity athletes and coaches, and the David L. MacIntosh Sport Medicine Clinic
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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 | 0.000 | 0.003 |
| 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 | 0.000 | 0.000 |
| 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".