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 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 | 0.013 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".