Prognostic utility of the Montreal Cognitive Assessment
Bibliographic record
Abstract
The Montreal Cognitive Assessment (MoCA) is a widely applied cognitive screening instrument, with a supplemental Memory Index Score (MIS) which has been suggested to predict conversion from Mild Cognitive Impairment (MCI) to Alzheimer's Clinical Syndrome (ACS). This study compared the prognostic utility of the MIS to other MoCA metrics in predicting conversion to ACS or other dementias (OD). We analyzed National Alzheimer's Coordinating Center data from 2900 participants aged 50 years or older, diagnosed with MCI at baseline, with at least one follow-up visit. Multinomial logistic regression models assessed whether baseline MoCA Total Score (TS) or MIS predicted final diagnoses, and receiver operating characteristic (ROC) curves examined the clinical utility of baseline MoCA TS, MIS, Free Recall Score (FRS) and TS+MIS for identifying ACS converters at 1-, 3-, and 5-year follow-ups. Over an average follow-up of 4.65 years, 26.5% converted to ACS and 7.4% to OD. Higher baseline TS was associated with lower odds of conversion to ACS (OR = 0.82) and OD (OR = 0.86), while higher MIS was associated with lower odds of ACS (OR = 0.82) but not OD (OR = 0.97). For identifying ACS, ROC area under the curve ranges showed modest advantage for FRS (0.70-0.73), MIS (0.71-0.74), and TS+MIS (0.70-0.74) over the TS (0.63-0.70). MoCA memory subscores were the strongest baseline indicator of later ACS conversion, but no cut-off score displayed acceptable sensitivity and specificity. Future research may explore if MoCA memory subscores display greater prognostic utility in combination with other ACS features.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".