Questionable utility of the Montreal Cognitive Assessment (MoCA) in detecting cognitive impairment in individuals with comorbid PTSD and SUD
Bibliographic record
Abstract
Posttraumatic stress disorder (PTSD) is frequently comorbid with substance use disorder (SUD) in individuals seeking treatment for substance use. Further, SUD and PTSD are individually associated with cognitive impairment (CI) and poor treatment outcomes. Despite the frequent use of the Montreal Cognitive Assessment (MoCA) as a screening tool for CI, the validity of the MoCA has not been established in individuals with comorbid SUD-PTSD. We assessed the criterion validity of the MoCA in 128 participants seeking inpatient medically-assisted detoxification using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) as a reference for CI. The correlation between the RBANS and MoCA was weaker in those with SUD-PTSD (r = .32) relative to SUD alone (r = .56). Receiver operating characteristic (ROC) curves demonstrated that the MoCA had moderate-to-high ability to discriminate CI in individuals with SUD alone, with an area under the ROC curve of .82 (95% CI .69–.92) and optimal cutoff score of ≤23. However, in individuals with comorbid SUD-PTSD, the ROC analysis was not significant. Results suggest that PTSD, when comorbid with SUD, reduces the criterion-related validity of the MoCA. We recommend exercising caution when classifying CI in individuals with SUD-PTSD using the MoCA and suggest reducing the cutoff score to ≤23 in order to limit the rate of false-positive CI diagnoses in SUD-PTSD populations.
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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 | 0.012 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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".