The Montreal Cognitive Assessment (MoCA) as a cognitive screen in addiction health care: A validation study for clinical practice
Bibliographic record
Abstract
Objective: The current study assessed the criterion validity of the Montreal Cognitive Assessment (MoCA) as a short cognitive screen for use in addiction health care. Method: Eighty-two patients were assessed with two parallel versions of the MoCA; at intake (baseline) and directly preceding an extensive neuropsychological assessment (NPA) approximately 8 weeks later (follow-up). Results: Of all included patients, 54.9% were classified as having substance-induced neurocognitive disorder. The most common primary substance of abuse was alcohol (70.7%). The criterion validity was determined predictively and concurrently, and sensitivities of .56 and .67 and specificities of .62 and .73 were found, respectively. Conclusion: While the MoCA is an adequate screen when administered at the same time as the NPA, the predictive validity of administering this cognitive screen at intake is limited. Furthermore, the relation between MoCA domain scores and the performance on their corresponding cognitive domain in the NPA is more reliable when the MoCA is administered at the same time as the NPA. While the MoCA can be used to screen for cognitive impairments in patients in addiction health care, the instrument’s sensitivity is not optimal, which should be taken into account when interpreting results.
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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 | 0.018 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".