Logical memory, visual reproduction, and verbal paired associates are effective embedded validity indicators in patients with traumatic brain injury
Bibliographic record
Abstract
OBJECTIVE: This study was design to evaluate the potential of the recognition trials for the Logical Memory (LM), Visual Reproduction (VR), and Verbal Paired Associates (VPA) subtests of the Wechsler Memory Scales-Fourth Edition (WMS-IV) to serve as embedded performance validity tests (PVTs). METHOD: The classification accuracy of the three WMS-IV subtests was computed against three different criterion PVTs in a sample of 103 adults with traumatic brain injury (TBI). RESULTS: The optimal cutoffs (LM ≤ 20, VR ≤ 3, VPA ≤ 36) produced good combinations of sensitivity (.33-.87) and specificity (.92-.98). An age-corrected scaled score of ≤5 on either of the free recall trials on the VPA was specific (.91-.92) and relatively sensitive (.48-.57) to psychometrically defined invalid performance. A VR I ≤ 5 or VR II ≤ 4 had comparable specificity, but lower sensitivity (.25-.42). There was no difference in failure rate as a function of TBI severity. CONCLUSIONS: In addition to LM, VR, and VPA can also function as embedded PVTs. Failing validity cutoffs on these subtests signals an increased risk of non-credible presentation and is robust to genuine neurocognitive impairment. However, they should not be used in isolation to determine the validity of an overall neurocognitive profile.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".