A-233 Incremental Utility of the Montreal Cognitive Assessment (MoCA) Memory Index Score (MIS) for Detection of Cognitive Impairment
Bibliographic record
Abstract
Abstract Objective To investigate the incremental utility of the optional MoCA Memory Index Score (MIS) for detection of cognitive impairment. Method This cross-sectional study utilized data collected from a mixed clinical sample of 153 veterans referred for clinical neuropsychological evaluations at a VA hospital. The sample was 87% male (n = 133), with an average age of 63.23 years (range 20–91) and average education level of 13.6 years (range 6–20). All participants completed the MoCA, including the MIS items. Participants meeting criteria for mild (n = 66) or major neurocognitive disorder (n = 24) were classified as cognitively impaired (CI). Sixty-three participants who did not meet criteria for a neurocognitive disorder were classified as having no cognitive impairment (NCI). Chi square analysis and logistic regression were utilized to determine the sensitivity of the MoCA total score for detection of cognitive impairment, and to determine whether the MIS significantly improved classification accuracy. Results The MoCA total score was a significant predictor of cognitive impairment status (X2 = 40.92, p < .001), with 73% sensitivity, 67% specificity, and 71% classification accuracy. When the MIS was added, the model retained significance (X2 = 41.13, p < .001), but overall sensitivity, specificity, and classification accuracy were unchanged; MIS was not a significant predictor in the combined model. Conclusions The optional MIS score did not significantly improve the sensitivity of the MoCA for detection of cognitive impairment.
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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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".