Assessment of symptoms in a concussion management programme: Method influences outcome
Bibliographic record
Abstract
CONTEXT: Monitoring of subjective symptoms is the foundation of all sport concussion management programmes. The purpose of this study is to examine methodological variables that impact symptom reporting during baseline testing. OBJECTIVE: To investigate how the administration method of a concussion assessment tool (self-report vs interview) affects the report of symptoms. DESIGN, SETTING AND PARTICIPANTS: This was a cross-sectional, semi-randomized study of 117 athletes. MAIN OUTCOME MEASUREMENTS: Subjects completed the Post-Concussion Scale during pre-season evaluations. RESULTS: A two-factor ANOVA revealed a significant difference in total symptom scores (p = 0.02) and number of endorsed symptoms (p = 0.02) across administration modes. Athletes had a greater total symptom score and reported a greater number of symptoms in the self-administration condition than in the interview condition. Furthermore, there was a significant difference in symptom reporting across interviewer gender. Athletes endorsed more symptoms when the interviewer was a woman. CONCLUSIONS: Because the method of collecting symptoms, as well as interviewer gender, can impact test results, self-report measures may be a better way of obtaining consistent results. Clinicians and researchers should be aware that both the nature and extent of symptom reporting is greater when using questionnaires than when athletes are interviewed.
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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 | 0.035 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".