Bibliographic record
Abstract
Objective Analysis of a proposed method for classifying invalid baseline tests in professional football players. Design Retrospective cross-sectional study. Setting Professional Canadian Football. Participants Four hundred and seventy-eight male football players from the Canadian Football League. Intervention A proposed system to classify invalid baselines used the lowest 5% of domain scores from baseline Immediate Post-concussion Assessment and Cognitive Testing (ImPACT), King Devick, and SCAT3 (balance and SAC totals) coupled with the highest 5% of total symptom report from SCAT3. Impairment on 2 or more scores was classified as invalid (INV). Main Outcome measures Descriptive statistics, correlation and stepwise regression analysis. Main Results Correlations revealed significant relationships (p<0.05) between INV and all 5 domains of ImPACT and SCAT3 symptom score, but not for King Devick and SCAT3 balance and SAC totals. The forward stepwise regression yielded a significant model (R2=0.23, F(4, 473)=35.7, p<0.001). Significant predictors included visual memory, reaction time, impulse control, and verbal memory from ImPACT. This system classified 3.7% of participant with invalid baseline tests. Conclusions There are few published methodologies used to classify invalid baseline tests in spite of their widespread use. Valid baseline tests are essential for proper identification and management of concussion. Our proposed method identified 4 ImPACT domains scores that predict invalid baseline scores. This system may be helpful in classifying invalid baseline tests with commonly used baseline test measures although more research is required to validate this method. Competing interests None.
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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 | 0.015 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".