Association of early versus late care seeking for sport-related concussion in adolescent athletes in Canada: a historical cohort study
Bibliographic record
Abstract
Objectives: This study aims to examine the association of time to recovery between early versus late presentation to outpatient community-based concussion management clinics following sport-related concussion (SRC) among adolescent Canadian athletes. Methods: Using electronic health records (between January 2017 and December 2019) from the Complete Concussion Management Inc (CCMI) database, this was a historical cohort study of Canadian athletes aged 12-18 presenting for care early (0-7 days) or late (8-28 days) after SRC. Time-to-recovery was defined as the date of clinician clearance to return to sport. Propensity scores were first derived from logistic regression with early versus late clinical presentation as the outcome. Cox proportional hazards regression analysis was then used to model the relationship between early versus late clinical presentation and time to recovery, while including the propensity score to adjust for confounding. The association was expressed using hazard rate ratios (HRR) with 95% CIs. Results: A total of 4696 patient records (mean age of 14.71 (±1.69 SD); 57.7% male) were eligible. Early presentation to a concussion management clinic following SRC was associated with faster time to recovery (adjusted HRR 1.23; 95% CI 1.14 to 1.32, p<0.001). This association was consistent within each quintile of the propensity score. The median time to recovery was 18 versus 22 days in the early and late groups, respectively. Conclusion: Adolescent athletes with SRC have favourable recovery trajectories when presenting for care up to 28 days. Time to recovery (clinician clearance to return to sport) may be quicker with an earlier presentation which can lead to a faster return to sport.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".