Concussion Symptoms Predictive of Adolescent Sport-Related Concussion Injury
Bibliographic record
Abstract
OBJECTIVE: To assess the predictive capability of the postconcussion symptom scale (PCSS) of the sport concussion assessment tool (SCAT) III to differentiate concussed and nonconcussed adolescents. DESIGN: Retrospective. SETTING: Tertiary. PARTICIPANTS: Sixty-nine concussed (15.2 ± 1.6 years old) and 55 control (14.4 ± 1.7 years old) adolescents. INDEPENDENT VARIABLES: Postconcussion symptom scale. MAIN OUTCOME MEASURE: Two-proportion z-test determined differences in symptom endorsement between groups. To assess the predictive power of the PCSS, we trained an ensemble classifier composed of a forest of 1000 decision trees to classify subjects as concussed, or not concussed, based on PCSS responses. The initial classifier was trained on all 22-concussion symptoms addressed in the PCSS, whereas the second classifier removed concussion symptoms that were not statistically significant between groups. RESULTS: Concussion symptoms common between groups were trouble falling asleep, more emotional, irritability, sadness, and anxious. After removal, analysis of the second classifier indicated that the 5 leading feature rankings of symptoms were headache, head pressure, light sensitivity, noise sensitivity, and "don't feel right," which accounted for 52% of the variance between groups. CONCLUSIONS: Collectively, self-reported symptoms through the PCSS can differentiate concussed and nonconcussed adolescents. However, predictability for adolescent patients may be improved by removing emotional and sleep domain symptoms.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".