False-Positive Error Rates for Reliable Digit Span and Auditory Verbal Learning Test Performance Validity Measures in Amnestic Mild Cognitive Impairment and Early Alzheimer Disease
Bibliographic record
Abstract
OBJECTIVE: The objective is to examine failure on three embedded performance validity tests [Reliable Digit Span (RDS), Auditory Verbal Learning Test (AVLT) logistic regression, and AVLT recognition memory] in early Alzheimer disease (AD; n = 178), amnestic mild cognitive impairment (MCI; n = 365), and cognitively intact age-matched controls (n = 206). METHOD: Neuropsychological tests scores were obtained from subjects participating in the Alzheimer's Disease Neuroimaging Initiative (ADNI). RESULTS: RDS failure using a ≤7 RDS threshold was 60/178 (34%) for early AD, 52/365 (14%) for MCI, and 17/206 (8%) for controls. A ≤6 RDS criterion reduced this rate to 24/178 (13%) for early AD, 15/365 (4%) for MCI, and 7/206 (3%) for controls. AVLT logistic regression probability of ≥.76 yielded unacceptably high false-positive rates in both clinical groups [early AD = 149/178 (79%); MCI = 159/365 (44%)] but not cognitively intact controls (13/206, 6%). AVLT recognition criterion of ≤9/15 classified 125/178 (70%) of early AD, 155/365 (42%) of MCI, and 18/206 (9%) of control scores as invalid, which decreased to 66/178 (37%) for early AD, 46/365 (13%) for MCI, and 10/206 (5%) for controls when applying a ≤5/15 criterion. Despite high false-positive rates across individual measures and thresholds, combining RDS ≤ 6 and AVLT recognition ≤9/15 classified only 9/178 (5%) of early AD and 4/365 (1%) of MCI patients as invalid performers. CONCLUSIONS: Embedded validity cutoffs derived from mixed clinical groups produce unacceptably high false-positive rates in MCI and early AD. Combining embedded PVT indicators lowers the false-positive rate.
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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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| 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".