Montreal Cognitive Assessment Memory Index Score (MoCA‐MIS) as a Predictor of Conversion from Mild Cognitive Impairment to <scp>A</scp> lzheimer's Disease
Bibliographic record
Abstract
OBJECTIVES: To assess the usefulness of the Montreal Cognitive Assessment (MoCA) total score (MoCA-TS) and Memory Index Score (MoCA-MIS) in predicting conversion to Alzheimer's disease (AD) in individuals with mild cognitive impairment (MCI). DESIGN: Retrospective chart review. SETTING: Community-based memory clinic. PARTICIPANTS: Individuals meeting Petersen's MCI criteria (N = 165). MEASUREMENTS: Baseline MoCA scores at MCI diagnosis were collected from charts of eligible individuals with MCI, and MoCA-TS, MoCA-MIS, and a cognitive domain index score were calculated to assess their prognostic value in predicting conversion to AD. RESULTS: One hundred fourteen participants progressed to AD (MCI-AD), and 51 did not (nonconverters; MCI-NC); 90.5% of participants with MCI with a MoCA-TS less than 20/30 and a MoCA-MIS less than 7/15 at baseline converted to AD within the average follow-up period of 18 months, compared with 52.7% of participants with MCI above the cutoffs on both scores. Individuals with multiple-domain amnestic MCI had the highest AD conversion rates (73.9%). CONCLUSION: Identifying individuals with MCI at high risk of conversion to AD is important clinically and for selecting appropriate subjects for therapeutic trials. Individuals with MCI with a low MoCA-TS and a low newly devised memory index score (MoCA-MIS) are at greater risk of short-term conversion to AD.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".