The Montreal Cognitive Assessment (MoCA) in geriatric rehabilitation: psychometric properties and association with rehabilitation outcomes
Bibliographic record
Abstract
BACKGROUND: Cognitive status has been reported to be an important predictor of rehabilitation outcome. The Montreal Cognitive Assessment (MoCA) was designed to overcome some of the limitations of established cognitive screening tools such as the Mini-Mental State Examination (MMSE). The purpose of this study is to evaluate the psychometric characteristics of the MoCA as a screening tool in a geriatric rehabilitation program and its ability to predict rehabilitation outcome. METHODS: Forty-seven geriatric rehabilitation program patients participated in the study. Assessments of each patient's functional (Functional Independence Measure) and cognitive status (MMSE and MoCA) were performed. Information on discharge destinations were obtained and rehabilitation efficacy and efficiency scores were calculated. RESULTS: Significant correlations were found between the MoCA and other cognitive status measures. Cognitive status at admission and successful rehabilitation were also associated. Defining rehabilitation success on the basis of relative functional efficacy (an indicator that includes the patient's potential for improvement), the sensitivity and specificity of the MoCA were 80% and 30% respectively. The attention subscale of the MoCA was also uniquely predictive of rehabilitation success. The attention subscale (cutoff 5/6) of the MoCA had a sensitivity of 40% and specificity of 90%, as did the MMSE. CONCLUSIONS: As a cognitive screening tool, the MoCA appears to have acceptable psychometric properties. Results suggest that the MoCA can have a considerable advantage over the MMSE in sensitivity and equivalence in specificity using both total and attention scale scores. The MoCA may be a more useful measure for detecting cognitive impairment and predicting rehabilitation outcome in this population.
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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 | 0.024 |
| 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 | 0.001 | 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".