Accuracy of the Montreal Cognitive Assessment tool for detecting mild cognitive impairment: A systematic review and meta‐analysis
Bibliographic record
Abstract
INTRODUCTION: This systematic review evaluates the accuracy of the Montreal Cognitive Assessment (MoCA) for detecting mild cognitive impairment (MCI). METHODS: We searched MEDLINE, PSYCInfo, EMBASE, and Cochrane CENTRAL (1995-2021) for studies comparing the MoCA with validated diagnostic criteria to identify MCI in general practice. Screening, data extraction, and risk of bias assessment were performed independently, in duplicate. Pooled sensitivity and specificity for MoCA cutoffs were estimated using bivariate meta-analysis. RESULTS: Thirteen studies [2158 participants, 948(44%) with MCI] were included; 10 used Petersen criteria as the reference standard. Risk of bias of studies were high or unclear for all domains except reference standard. Sensitivity and specificity were 73.5%(95% confidence interval: 56.7-85.5) and 91.3%(84.6-95.3) at cutoff <23; 79.5%(67.1-88.0) and 83.7%(75.4-89.6) at cutoff <24; and 83.8%(75.6-89.6) and 70.8(62.1-78.3) at cutoff <25. DISCUSSION: MoCA cutoffs <23 to <25 maximized the sum of sensitivity and specificity for detecting MCI. The risk of bias of included studies limits confidence in these findings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.039 | 0.112 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.026 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".