Introducing the ImPACT-5: An Empirically Derived Multivariate Validity Composite
Bibliographic record
Abstract
OBJECTIVE: To create novel Immediate Post-Concussion and Cognitive Testing (ImPACT)-based embedded validity indicators (EVIs) and to compare the classification accuracy to 4 existing EVIImPACT. METHOD: The ImPACT was administered to 82 male varsity football players during preseason baseline cognitive testing. The classification accuracy of existing EVIImPACT was compared with a newly developed index (ImPACT-5A and B). The ImPACT-5A represents the number of cutoffs failed on the 5 ImPACT composite scores at a liberal cutoff (0.85 specificity); ImPACT-5B is the sum of failures on conservative cutoffs (≥0.90 specificity). RESULTS: ImPACT-5A ≥1 was sensitive (0.81), but not specific (0.49) to invalid performance, consistent with EVIImPACT developed by independent researchers (0.68 sensitivity at 0.73-0.75 specificity). Conversely, ImPACT-5B ≥3 was highly specific (0.98), but insensitive (0.22), similar to Default EVIImPACT (0.04 sensitivity at 1.00 specificity). ImPACT-5A ≥3 or ImPACT-5B ≥2 met forensic standards of specificity (0.91-0.93) at 0.33 to 0.37 sensitivity. Also, the ImPACT-5s had the strongest linear relationship with clinically meaningful levels of invalid performance of existing EVIImPACT. CONCLUSIONS: The ImPACT-5s were superior to the standard EVIImPACT and comparable to existing aftermarket EVIImPACT, with the flexibility to optimize the detection model for either sensitivity or specificity. The wide range of ImPACT-5 cutoffs allows for a more nuanced clinical interpretation.
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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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".