Presenting symptoms and recovery time among youth athletes with and without a history of concussion
Bibliographic record
Abstract
Objective Determine if youth athletes with a history of concussion had more symptoms, higher symptom score, longer recovery, or were more likely to be diagnosed with Post-Concussion Syndrome (PCS) than those with no history of concussion. Design Retrospective chart review. Setting Paediatric, multi-disciplinary concussion. Participants 306 youth (mean 14.0 years, SD 2.3; 65.5% male) with an acute sport-related concussion. Intervention (or assessment of risk factors) Self-reported concussion history. Outcome measures Recovery days were days between concussion and medical clearance by the neurosurgeon. Medical clearance occurred after tolerating full-time school, completing return-to-play protocol, and absence of vestibulo-ocular dysfunction. Initial symptoms were reported on the Post Concussion Symptom Severity score (PCSS); symptom severity was the total PCSS score. The neurosurgeon diagnosed Post-Concussion Syndrome (PCS) using ICD-10 criteria of symptoms for at least 30 days. Main results Median number of initial symptoms was 5.5 (IQR: 1–10) for youth with no concussion history and 7.0 (IQR: 2–14) with a concussion history (p=0.04). Initial median PCSS score was 9 (IQR: 1–22) for youth with no concussion history and 13 (IQR: 3–34) with a concussion history (p=0.03). Median recovery days was 22 (IQR: 15–43) for those with no concussion history compared to 23 (IQR: 16–39) with a concussion history (p=0.41). There was no significant difference in subsequently being diagnosed with PCS (no concussion history: 40.1%, ?concussion history: 41.7%, p=0.73). Conclusions Despite a significantly higher initial symptom burden among those with a concussion history, there was no increased risk of protracted recovery or subsequent diagnosis of PCS. Competing interests None.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".