Bibliographic record
Abstract
OBJECTIVES: Brief cognitive tests are recommended in clinical services outside of specialized memory clinics as case-finding tools to reduce the diagnostic gap of dementia. Although the Montreal Cognitive Assessment (MoCA) is among the most widely used brief tests in specialized memory clinics, its length precludes routine use in nonspecialty clinics. This study investigated whether a small subset of MoCA would suffice to match the performance of the full MoCA in detecting dementia and, hence, be useful in nonspecialty clinics. DESIGN: Cross-sectional test research. SETTING: Alzheimer's Disease Centers across the United States. PARTICIPANTS: Participants age ≥65 years (n = 8773). MEASURES: Participants completed MoCA and were evaluated for dementia. The study sample was split into 2: the derivation sample (n = 4386) was used to develop a short variant of MoCA that best distinguish dementia (using the best-subset-approach with 10-fold cross-validation), while the validation sample (n = 4387) verified its actual performance using area under the receiver operating characteristic-curve (AUC). RESULTS: A 4-item cognitive test was identified, comprising Clock-drawing, Tap-at-letter-A, Orientation, and Delayed-recall. It demonstrated excellent performance in distinguishing dementia from nondementia (AUC 94.2%) and was comparable to that of MoCA (AUC 93.8%), even across education subgroups. It explained 85.9% of the variability in MoCA and had scores that could be mapped to MoCA with reasonable precision. At the optimal cut-off score of <10, it demonstrated 87.9% sensitivity and 87.6% specificity in detecting dementia. CONCLUSIONS AND IMPLICATIONS: Using rigorous methods, this study developed a brief cognitive test that is free of charge, takes <5 minutes to complete, covers the key cognitive domains, and has standardized instructions to allow its administration even by nonphysicians. This brief test is well suited as a case-finding tool in nonspecialty clinics (such as in primary care and geriatric clinics) and may improve care-integration with specialized memory clinics that utilize MoCA.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".