Performance Validity Testing in Patients with Substance Abuse in Addiction Care
Bibliographic record
Abstract
BACKGROUND: Low performances on neuropsychological tests are common in patients with substance use disorder (SUD), indicating potential cognitive impairments that may significantly impact treatment engagement and prognosis. While neuropsychological assessment is crucial for identifying these cognitive deficits, to date, data on the performance validity of individuals in addiction care is lacking. Performance validity testing (PVT) can be used to assess the accuracy of such test results. OBJECTIVES: This study examined the prevalence of suboptimal performance on different PVTs in a SUD inpatient population, their agreement in detecting poor performance validity, and their association with overall cognitive performance. METHODS: Retrospective data were analyzed from 172 SUD inpatients (2017-2024) in an addiction care clinic. Three PVTs were examined: the Visual Association Test-Extended (VAT-E), the Amsterdam Short-Term Memory test (ASTM), and the WAIS-IV Digit Span Age-Corrected Scaled Score (DS ACSS). Failure rates were calculated, and correlations between PVT outcomes and between the PVT measures and Montreal Cognitive Assessment (MoCA) were computed. RESULTS: Failure rates varied substantially across PVTs (from 1.3-36%). Agreement between PVTs was low (κ-values 0.019-0.397), with minimal correlations between ASTM, DS ACSS, and VAT-E scores. Weak to moderate positive correlations (ρ-values -0.024-0.403) were found between PVTs and the MoCA. CONCLUSIONS/IMPORTANCE: The variability in failure rates suggests that different PVTs may not measure the same construct. Possibly, the ASTM may be too challenging for many patients and DS ACSS failures may reflect lower intellectual abilities rather than true non-credible performance. This stresses the importance of selecting appropriate PVTs in addiction care settings to avoid misclassification and ensure valid neuropsychological assessments.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".