Clinical value of the Montreal Cognitive Assessment (MoCA) in patients suspected of cognitive impairment in old age psychiatry. Using the MoCA for triaging to a memory clinic
Bibliographic record
Abstract
Objectives: Diagnostic pathways are limited. A validated instrument that can triage patients when they are suspected of mild dementia (MD) is necessary to optimise referrals.Method: The MoCA is validated for identifying MD and mild cognitive impairment (MCI) in a cohort of patients suspected of cognitive impairment (CI) after initial assessment in old age psychiatry. The reference standard was the consensus-based diagnoses for MD and MCI, adhering to the international criteria and using suspected patients, but without CI as comparisons (NoCI).Results: The mean MoCA scores differ significantly between the groups: 24(SE: .59) in NoCI, 21(SE: .31) in MCI and 16,7(SE: .45) in MD (p < .05). The AUC of MD against non-demented (MCI + NoCI) was 0.83(95%CI: 0.78–0.88) resulting in 90% sensitivity, 65% specificity, 50%PPV and 94%NPV at a “best” cutoff of <21 according the Youden index and respectively 0.77(95%CI: 0.69–0.85), 56%, 73%, 90%, 28% for CI (MD + MCI) against NoCI at <21.Conclusion: 90% of individuals with a MoCA of <21 will have CI (MD + MCI), while 94% with a MoCA of ≥21 will not have dementia. The MoCA can reduce referrals substantially (50%) by selecting who don’t need further work up in a memory clinic, even if they were suspected of CI after initial assessment.
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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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".