Patients presenting to an outpatient sport medicine clinic with concussion
Bibliographic record
Abstract
Objective To describe the characteristics of patients who presented to outpatient sport and exercise medicine clinics with concussion. Design Retrospective chart review of electronic medical records. Setting Three specialized sport and exercise medicine clinics in London, Ont. Participants A total of 283 patients presenting with concussion. Main outcome measures Data collected included demographic variables (age and sex), sport participation at the time of injury, previous medical history (including history of concussion), Post-Concussion Symptom Scale (PCSS) scores, and return-to-play (RTP) variables (delay and outcome). Results The mean age of patients presenting for care was 17.6 years; 70.9% of patients were younger than 18 years of age (considered pediatric patients); 58.8% of patients were male; and 31.7% of patients had a previous history of concussion. The main sports associated with injury were hockey (40.0%), soccer (12.6%), and football (11.7%). Return to play was granted to 50.9% of patients before the 3-week mark and 80.2% of patients before 8 weeks. Total PCSS scores (maximum score was 132) and neck scores (part of the PCSS, maximum score was 6) were significantly higher in adults compared with pediatric patients (36.2 vs 27.6, P = .02, and 1.8 vs 1.2, P = .02, respectively). A significant difference was seen in RTP, with pediatric patients returning earlier than adults did ( P = .04). This difference was not seen when comparing males with females ( P = .07). Longer duration of follow-up did not influence RTP outcomes. Previous history of concussion was associated with restriction from contact or collision sports ( P < .001). Conclusion Given the age and sex variability found in this study, as well as in previous published reports, it is important to manage each patient individually using current best available practice strategies to optimize long-term outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".