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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".