A Cross-Sectional Decision-Making Approach to Inform Neuropsychological Battery Development in Professional Hockey
Bibliographic record
Abstract
OBJECTIVE: Neuropsychologists commonly use a large battery of tests to inform clinical decisions. Decision analysis can be used to determine which individual tests play a role in the decision-making process. The objective of this project was to conduct quantitative and qualitative decision analysis of decisions by team neuropsychologists with professional hockey players being evaluated as part of the National Hockey League (NHL)/NHL Players Association Concussion Protocol. METHOD: We extracted neuropsychological data from an NHL clinical program database. Team neuropsychologists evaluated concussed players using a hybrid neuropsychological test battery. The neuropsychologists then determined whether players were experiencing concussion-related cognitive difficulties. Logistic regression was used to examine which tests accounted for unique variance in the decision-making process. We also conducted a survey of NHL neuropsychologists, asking them to rate the usefulness of each test in the battery. RESULTS: Five of the fifteen measures accounted for unique variance in team neuropsychologists' decisions, including the ImPACT Verbal Memory Composite, Visual Motor Composite, Reaction Time Composite, Symptom Score, and Brief Visuospatial Memory Test-Revised Delayed Recall. Notable discrepancies were uncovered between quantitative indications of usefulness and self-reported qualitative perceptions of test usefulness when making decisions. Qualitatively, clinicians reported that the Hopkins Verbal Learning Test-Revised, Symbol Digit Modalities Test, ImPACT Reaction Time, and Color Trails 2 were the most useful tests when making decisions. CONCLUSIONS: Along with validation studies, decision analysis can be used as part of a comprehensive evaluation process to inform the development of best-practice batteries for use among athletes with sports 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 | 0.081 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".