Defining and Validating a Short Form Montreal Cognitive Assessment (s-MoCA) for Use in Neurodegenerative Disease (S1.005)
Bibliographic record
Abstract
Objective: To propose a clinically useful, short form of the Montreal Cognitive Assessment (MoCA) applicable to a broad range of neurological disorders. Background: Screening for cognitive deficits is essential in neurodegenerative disease. Screening tests, such as the Montreal Cognitive Assessment (MoCA), are easily administered, correlated with neuropsychological performance, and demonstrate diagnostic utility. Yet, administration time is suboptimal in many clinical settings. Methods: Item response theory and computerized adaptive testing simulation were employed to establish an abbreviated MoCA in 1,850 well-characterized community-dwelling individuals with and without neurodegenerative disease (e.g. AD, MCI, PD, PD-MCI, etc.). Results: Eight MoCA items with high item discrimination and appropriate difficulty were identified for use in a short form (s-MoCA). The s-MoCA was highly correlated with the original MoCA (Pearson r=0.959 (95[percnt]CI: 0.956-0.962)), showed robust diagnostic classification, and cross-validation procedures substantiated these items. Conclusions: Early detection of cognitive impairment is an important clinical and public health concern. Yet, administration of screening measures is limited by time constraints in demanding clinical settings. Here, we provide a short form of the MoCA that is valid across neurological disorders and can be administered in approximately 5-minutes. Items selected for the s-MoCA span several neurocognitive domains, a direct reflection of the standard MoCA. Early detection of cognitive impairment is becoming an important clinical and public health concern. The benefits of early detection are immense, and include, but are not limited to: 1) the identification of clinical and daily functioning concerns (e.g. falls, driving); 2) providing patients and families an opportunity to plan ahead medically, financially and legally; 3) offering early screening once disease-modifying therapies for neurocognitive disease become available; and 4) reducing long-term health-care costs.
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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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".