A - 34 Differences in Symptom and Well-Being Report at Initial Post-Injury Concussion Medical Evaluations
Bibliographic record
Abstract
Abstract Purpose To investigate differences in subjective symptom report and percentage ratings of subjective well-being (SWB) at initial post-injury concussion medical evaluations in professional football players. Method A retrospective, quasi-experimental design was used for the study. Participants included Canadian Football League players who underwent initial locker room post-injury medical evaluations. CFL concussion protocol requires any player suspected of a concussion to undergo a full evaluation including SCAT5. There were 2 groups including 43 players subsequently diagnosed with concussions who were compared with 22 players not diagnosed with concussion. Data included total symptom score from the SCAT5 and SWB (ratings out of 100%). T-tests and descriptive statistics evaluated differences between groups. Results At baseline, there were no significant differences on ratings of SWB between groups. In contrast, there was a significant difference in ratings of subjective well being between concussed players (M = 75.5%, SD = 19.5) and non concussed players (M = 92.7, SD = 7.7); t(63) = [3.8], p = [0.1]. Furthermore, significant differences on total symptom report from the SCAT5 were identified between concussed players (M = 16.9, SD = 15.4) and non concussed players (M = 3.5, SD = 5.7); t(63) - [3.9], p = [<0.01]. The sensitivity was 85% with a specificity of 63% using a post-injury SWB score of 75%. Conclusions Results indicated that at initial post-injury concussion evaluations, players subsequently diagnosed with concussion have lower SWB. Further research is needed to determine whether this metric would be helpful in the diagnosis of concussion.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".