Cognitive, Sleep-Arousal, Physical, and Affective Domain Scores on the Post-Concussion Symptom Scale: Added Utility in Detecting Symptom Elevations among Student-Athletes with a Remote History of Concussion
Bibliographic record
Abstract
OBJECTIVE: The evaluation of self-reported symptoms is a standard component of concussion assessment and management. Clinicians typically evaluate a total symptom severity score rather than scores corresponding to specific symptom domains (i.e., cognitive, sleep-arousal, physical, and affective symptoms). This study examined (i) whether elevations in specific symptom domains would be missed when interpreting only the total symptom severity score and (ii) if a single symptom domain elevation was more common than having elevated symptoms across multiple domains. METHOD: Adolescent student-athletes (N = 1,008) with concussion history (i.e., ≥6 months since last concussion) completed the Post-Concussion Symptom Scale (PCSS). The PCSS total score and cognitive, sleep-arousal, physical, and affective domain scores were calculated. To determine if symptoms were elevated, scores were compared to normative data matched on gender and pre-existing conditions, with scores considered elevated if they were ≥84th percentile. The frequency of total and domain score elevations were calculated and stratified by gender and number of prior concussions (i.e., 1 or ≥2 prior concussions). RESULTS: Overall, 26% of student-athletes had an elevated symptom domain score without being elevated on the total score. The most common symptom presentation was to have a single elevated symptom domain (21%), followed by two (11%), three (8%), or four elevated domains (6%). CONCLUSIONS: Interpreting PCSS symptom domains may be beneficial in detecting student-athletes with elevated symptoms following a remote concussion. Roughly a quarter of student-athletes have domain-specific symptom elevations that would be missed by interpreting the total score alone.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".