Diagnostic accuracy of the Montreal Cognitive Assessment (MoCA) for cognitive screening in old age psychiatry: Determining cutoff scores in clinical practice. Avoiding spectrum bias caused by healthy controls
Bibliographic record
Abstract
OBJECTIVE/METHODS: The Montreal Cognitive Assessment (MoCA) is an increasingly used screening tool for cognitive impairment. While it has been validated in multiple settings and languages, most studies have used a biased case-control design including healthy controls as comparisons not representing a clinical setting. The purpose of the present cross-sectional study is to test the criterion validity of the MoCA for mild cognitive impairment (MCI) and mild dementia (MD) in an old age psychiatry cohort (n = 710). The reference standard consists of a multidisciplinary, consensus-based diagnosis in accordance with international criteria. As a secondary outcome, the use of healthy community older adults as additional comparisons allowed us to underscore the effects of case-control spectrum-bias. RESULTS: The criterion validity of the MoCA for cognitive impairment (MCI + MD) in a case-control design, using healthy controls, was satisfactory (area under the curve [AUC] 0.93; specificity of 73% less than 26), but declined in the cross-sectional design using referred but not cognitive impaired as comparisons (AUC 0.77; specificity of 37% less than 26). In an old age psychiatry setting, the MoCA is valuable for confirming normal cognition (greater than or equal to 26, 95% sensitivity), excluding MD (greater than or equal to 21; negative predictive value [NPV] 98%) and excluding MCI (greater than or equal to 26;NPV 94%); but not for diagnosing MD (less than 21; positive predictive value [PPV] 31%) or MCI (less than 26; PPV 33%). CONCLUSIONS: This study shows that validating the MoCA using healthy controls overestimates specificity. Taking clinical and demographic characteristics into account, the MoCA is a suitable screening tool-in an old age psychiatry setting-for distinguishing between those in need of further diagnostic investigations and those who are not but not for diagnosing cognitive impairment.
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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 | 0.030 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".