Montreal Cognitive Assessment (MoCA) scores in medically compromised patients: A scoping review.
Bibliographic record
Abstract
OBJECTIVE: The purpose of this review is to critically examine studies that have examined investigated the Montreal Cognitive Assessment (MoCA) and functional or medical outcomes and other health variables in patients with non-neurologic medical conditions. METHOD: Databases OVID Medline and Embase were systematically searched through April 2020, yielding 281 articles that were separately screened for inclusion. Study characteristics extracted from retained articles are presented in Table S1 (online supplemental materials). RESULTS: Thirty-six articles were retained. Cognitive impairment as assessed by the MoCA was associated with adverse health variables including increased morbidity/mortality, poorer functional abilities, increased length of hospital stay, and increased hospital readmissions in 34 of 36 articles. CONCLUSIONS: Cognitive impairment as detected by the MoCA was shown in 34 of 36 studies to be associated with worse functional or medical status compared to those with better cognitive functioning across a variety of medical populations. Further research is needed to better understand how to best use the MoCA to potentially inform treatment planning in medical populations, including referral for more detailed neuropsychological evaluation. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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 | 0.008 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".