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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".