6.11 Dizziness, neck pain and headaches as predictors of recovery following sport-related concussion
Bibliographic record
Abstract
Objective To evaluate symptoms as predictors of recovery following sport-related concussion (SRC). Design Prospective cohort. Setting Acute sport concussion clinic (ASCC) in Calgary, Alberta, Canada. Participants Patients aged 13–60 years who were diagnosed with SRC at the ASCC. Interventions (or Assessment of Risk Factors) Symptoms reported on the Post Concussion Symptom Scale (PCSS) on the Sport Concussion Assessment Tool (SCAT). Outcome Measures Participants completed a questionnaire [including sex (male/female), previous concussion, medical history] and the SCAT3/5. Participants were seen by a sport medicine physician and athletic therapist or physiotherapist and followed until medical clearance to return to sport. Concussion and medical clearance were defined as per the 5th International Consensus Conference on Concussion in Sport. A cox proportional hazard regression model was used to evaluate how the presence of dizziness, neck pain or headache (DNHA) related to the outcome of medical clearance at 90 days (yes/no). Main Results A total of 314 participants [161 (51.3%) youth, 153 (48.7%) adults; 159 (50.6%) male, 155 (49.4%) female; mean age 24.1 (95% CI 22.7–25.4)] participated in this study. Medical clearance at 90 days was achieved by 162 participants (51.6%). One of DNHA was reported by 49 (15.6%), two of DNHA by 85 (27.1%), all three of DNHA by 146 (46.5%) and none of DNHA by 34 (10.8%). Participants with one of DNHA were 0.35 (0.21–0.59), two of DNHA 0.18 (0.11–0.31) and all three 0.18 (0.12–0.29) times less likely to recover within 90 days compared to participants with no DNHA. Conclusions Symptoms of DNHA may predict longer recovery from SRC. Further research to evaluate the mechanism is warranted.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".