Two baselines are better than one: Improving the reliability of computerized testing in sports neuropsychology
Bibliographic record
Abstract
Computerized neuropsychological tests are frequently used to assist in return-to-play decisions following sports concussion. However, due to concerns about test reliability, the Centers for Disease Control and Prevention recommends yearly baseline testing. The standard practice that has developed in baseline/postinjury comparisons is to examine the difference between the most recent baseline test and postconcussion performance. Drawing from classical test theory, the present study investigated whether temporal stability could be improved by taking an alternate approach that uses the aggregate of 2 baselines to more accurately estimate baseline cognitive ability. One hundred fifteen English-speaking professional hockey players with 3 consecutive Immediate Postconcussion Assessment and Testing (ImPACT) baseline tests were extracted from a clinical program evaluation database overseen by the National Hockey League and National Hockey League Players' Association. The temporal stability of ImPACT composite scores was significantly increased by aggregating test performance during Sessions 1 and 2 to predict performance during Session 3. Using this approach, the 2-factor Memory (r = .72) and Speed (r = .79) composites of ImPACT showed acceptable long-term reliability. Using the aggregate of 2 baseline scores significantly improves temporal stability and allows for more accurate predictions of cognitive change following concussion. Clinicians are encouraged to estimate baseline abilities by taking into account all of an athlete's previous baseline scores.
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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 | 0.042 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".