Development of embedded performance validity indicators in the NIH Toolbox Cognitive Battery.
Bibliographic record
Abstract
To assess noncredible performance on the NIH Toolbox Cognitive Battery (NIHTB-CB), we developed embedded validity indicators (EVIs). Data were collected from 98 adults (54.1% female) as part of a prospective multicenter cross-sectional study at 4 mild traumatic brain injury (mTBI) specialty clinics. Traditional EVIs and novel item-based EVIs were developed for the NIHTB-CB using the Medical Symptom Validity Test (MSVT) as criterion. The signal detection profile of individual EVIs varied greatly. Multivariate models had superior classification accuracy. Failing ≥4 traditional EVIs at the liberal cutoff or ≥3 at the conservative cutoff produced a good combination of sensitivity (.57 to .61) and specificity (.92 to .94) to MSVT. Combining the traditional and item-based EVIs improved sensitivity (.65 to .70) at comparable specificity (.91 to .95). In conclusion, newly developed EVIs within the NIHTB-CB effectively discriminated between patients who passed versus failed the MSVT. Aggregating EVIs within the same category into validity composites improved signal detection over univariate cutoffs. Item-based EVIs improved classification accuracy over that of traditional EVIs. However, the marginal gains hardly justify the burden of extra calculations. The newly introduced EVIs require cross-validation before wide-spread research or clinical application. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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 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".