B - 114 Evaluation of the Montreal Cognitive Assessment Memory Index Score for Predicting Amnestic Mild Cognitive Impairment to Alzheimer’s Clinical Syndrome Progression
Bibliographic record
Abstract
Abstract Objective The Montreal Cognitive Assessment Memory Index Score (MoCA-MIS) is derived from the MoCA as a supplementary memory measure, with research suggesting the MoCA-MIS can identify those at higher risk of progression from mild cognitive impairment (MCI) to Alzheimer’s Clinical Syndrome (ACS). This study evaluated the sensitivity and specificity of the MoCA total score (TS), delayed free recall score (DFRS), and MIS in classifying amnestic MCI (aMCI) to ACS converters and replicated an algorithm recommended by Julayanont et al. (2014). Methods National Alzheimer’s Coordinating Center data were examined in individuals with aMCI, ≥50 years of age (Myears = 75.35[7.92]) who had 3–6 annual follow-up visits. Participants (n = 353) were mostly male (58%), White (88%), and well-educated (Myears = 16.37[5.21]). Receiver operating characteristic (ROC) analyses utilized baseline MoCA scores to examine optimal cutoffs using Youden’s index. Sensitivity and specificity of Julayanont’s algorithm (TScutoff<20 + MIScutoff<7) were also examined. Results Over follow-up (Myears = 4.52[1.02]), 53.3% converted to ACS. ROC analyses to predict longitudinal conversion from aMCI to ACS revealed similar sensitivity and specificity across MoCA scores (TScutoff<24, AUC = 0.67, Sensitivity = 0.78, Specificity = 0.49; DFRScutoff<2, AUC = 0.67, Sensitivity = 0.75, Specificity = 0.54; MIScutoff<8, AUC = 0.69, Sensitivity = 0.66, Specificity = 0.64). The Julayanont algorithm displayed low sensitivity (0.18) but high specificity (0.93) in this sample. Conclusion In predicting aMCI to ACS conversion over a 3–6 year period, baseline MoCA-TS, DFRS, and MIS cutoffs failed to demonstrate high sensitivity with adequate specificity. The Julayanont combination approach had unacceptable sensitivity in this sample. These findings suggest MoCA cutoffs are poor predictors of future cognitive progression in those with aMCI and highlight the importance of comprehensive longitudinal follow-up.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".