Montreal Cognitive Assessment (MoCA) use in general practice for the early detection of cognitive impairment: a feasibility study
Bibliographic record
Abstract
BACKGROUND: GPs can detect cognitive impairment (CI) at a very early stage, allowing early support for people and their caregivers. The early onset of CI is between 50 years and 60 years. Currently, in France, the Mini-Mental State Examination (MMSE) remains the most used screening test, although it has a lower sensitivity and specificity than the Montreal Cognitive Assessment (MoCA) for detecting mild CI, taking an average of 15 minutes to complete. AIM: To investigate the feasibility of the MoCA during routine consultations in general practice for the early detection of CI and to determine prevalence of CI in a primary care setting. DESIGN & SETTING: A quantitative, prospective feasibility study was carried out in real-life working conditions during routine GP consultations in France. METHOD: GPs performed MoCA on adults aged ≥50 years, without suspected or confirmed CI. RESULTS: Sixty-one GPs performed 221 MoCA with a mean duration of 8 minutes and detected mild neurocognitive impairment in 62% of patients. CONCLUSION: The MoCA is feasible and easy to perform during routine consultations in general practice by trained and experienced physicians.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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".