Performance Validity in Collegiate Football Athletes at Baseline Neurocognitive Testing
Bibliographic record
Abstract
OBJECTIVE: To assess the prevalence of invalid performance on baseline neurocognitive testing using embedded measures within computerized tests and individually administered neuropsychological measures, and to examine the influence of incentive status and performance validity on neuropsychological test scores. SETTING: Sport-related concussion management program at a regionally accredited university. PARTICIPANTS: A total of 83 collegiate football athletes completing their preseason baseline assessment within the University's concussion management program and a control group of 140 nonathlete students. DESIGN: Cross-sectional design based on differential incentive status: motivated to do poorly to return to play more quickly after sustaining a concussion (athletes) versus motivated to do well due to incentivizing performance (students). MAIN MEASURES: Immediate Post-Concussion and Cognitive Testing (ImPACT), performance validity tests, and measures of cognitive ability. RESULTS: Half of the athletes failed at least 1 embedded validity indicator within ImPACT (51.8%), and the traditional neuropsychological tests (49.4%), with large effects for performance validity on cognitive test scores (d: 0.62-1.35), incentive status (athletes vs students; d: 0.36-1.15), and the combination of both factors (d: 1.07-2.20) on measures of attention and processing speed. CONCLUSION: Invalid performance on baseline assessment is common (50%), consistent across instruments (ImPACT or neuropsychological tests) and settings (one-on-one or group administration), increases as a function of incentive status (risk ratios: 1.3-4.0) and results in gross underestimates of the athletes' true ability level, complicating the clinical interpretation of the postinjury evaluation and potentially leading to premature return to play.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".