Using Variance to Explore the Diagnostic Utility of Baseline Concussion Testing
Bibliographic record
Abstract
The Graded Symptom Checklist (GSC), Standardized Assessment of Concussion (SAC), Balance Error Scoring System (BESS), and King-Devick Test (KDT) are considered important components of concussion assessment. Whether baseline testing improves the diagnostic utility of these tests remains unclear. We performed an observational cohort study to investigate the within-subject and between-subjects variability of these tests over repeated assessments during two football seasons to examine whether baseline testing reduces variability in test performance. Thirty-five male collegiate football players completed weekly clinical concussion assessments over two seasons. Within-subject (week-to-week) and between-subjects (player-to-player) variability for each test were compared using a bootstrap analysis. Within-subject and between-subjects proportions of overall variance for each test score were calculated. Mixed-model analyses were used to quantify practice effects resulting from repeated testing. For the GSC and BESS, within-subject and between-subjects variability did not significantly differ. For the KDT, the proportion of within-subject variance (20.2%) was significantly less than the between-subjects variance (79.8%). For SAC, however, the proportion of within-subject variance (66.8%) was significantly greater than the between-subjects variance (33.8%). A small, but significant, practice effect was observed for the BESS and KDT tests. When athletes are evaluated during a football season for concussion using the GSC, SAC, and BESS, comparing their scores to baseline performance is likely no more beneficial than comparing them to normative population data for identifying neurological changes associated with concussion. For the KDT, comparison to baseline testing is likely beneficial because of significantly higher between-subjects variability.
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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 | 0.060 | 0.172 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".