Comparison of Rapid Cognitive Screen against Montreal Cognitive Assessment in screening for cognitive impairment in the old and old‐old
Bibliographic record
Abstract
BACKGROUND: The Montreal Cognitive Assessment (MoCA) was developed as a screening tool for mild cognitive impairment (MCI). Given the need for a rapid screening test in settings such as primary care, we compare the validity of the Rapid Cognitive Screen (RCS) against the MoCA, and determine cut-off scores in the old and old-old. METHODS: Cross-sectional study involving community-dwelling 'old' (65 to 79 years old) and 'old-old' (≥ 80 years old) without dementia. Cognitive impairment was defined by MoCA score 17 to 22. Validation was done using the receiver operating characteristic (ROC) curve analysis: area under the curve (AUC), sensitivity (Sn), and specificity (Sp). RESULTS: Of the 183 participants (mean age 72.1 ± 5.2 years),15.8% (n = 29) were classified as cognitively impaired. The overall ROC curve had an AUC of 0.82 (95% CI 0.75-0.90, P < 0.01) with an optimal cut-off of 7/8 on RCS (Sn 0.77, Sp 0.72). The 'old' and 'old-old' group had AUC of 0.82 (95% CI 0.74-0.91, P < 0.01) with 8/9 as optimal cut-off (Sn 0.51, Sp 0.96) and AUC of 0.85 (95% CI 0.66-1.03, P < 0.01) with 7/8 as optimal cut-off (Sn 0.71, Sp 1.00) respectively. In multivariate analysis, age was associated with 0.05 (95% CI -0.10-0.00, P < 0.04) point decrement, while >6 years of education was associated with 0.82 (95% CI 0.32-1.33, P < 0.01) point increment in RCS scores. CONCLUSION: The three-item RCS is quick and easy to administer. Although RCS met the criterion for good validity against MoCA in predicting cognitive impairment, its utility as a first-line screening tool needs to be further validated in a large-scale population study.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 0.000 |
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".