A-70 * An Abbreviated MoCA to Differentiate Normal Cognition, Mild Cognitive Impairment, and Alzheimer's Disease
Bibliographic record
Abstract
Objective: To identify which Montreal Cognitive Assessment (MoCA) items are most sensitive to mild cognitive impairment (MCI) and Alzheimer disease (AD) and investigate the utility of an abbreviated form of the MoCA. Method: 408 subjects (normal: n = 152, MCI: n = 169, AD: n = 87) were randomly divided into two groups to derive and validate an abbreviated MoCA. Using the derivation sample, analysis of individual MoCA items determined which items were best able to differentiate the three diagnostic groups. Receiver Operating Characteristic (ROC) curves were used to evaluate and compare the diagnostic accuracy of the abbreviated MoCA, standard MoCA, and the MMSE in the derivation and cross-validation samples. Results: Serial subtraction (Cramer's V = .408), delayed recall (Cramer's V = .702), and orientation items (Cramer's V = .832) were included in the abbreviated MoCA based on largest effect sizes relative to other items. The abbreviated MoCA demonstrated a higher area under the curve (AUC) than the standard MoCA when differentiating between MCI and AD (AUC = .93 vs .89), as well as controls and AD (AUC = .98 vs .97). Additionally, the abbreviated MoCA showed a higher AUC than the MMSE when differentiating between MCI and AD (AUC = .93 vs .92), MCI and controls (AUC = .80 vs .70), and controls and AD (AUC = .98 vs .96). This general pattern of findings was confirmed in the cross-validation sample. Conclusion(s): Diagnostic accuracy of the abbreviated MoCA was generally superior to the MMSE and standard MoCA. These results suggest an abbreviated MoCA form could be an effective and efficient brief tool in detecting and characterizing 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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".