P3‐097: SHORTENED VERSIONS OF MONTREAL COGNITIVE ASSESSMENT (MOCA) ARE FEASIBLE FOR SCREENING OF MILD COGNITIVE IMPAIRMENT AND DEMENTIA: A SYSTEMATIC REVIEW AND META‐ANALYSIS OF 33 CROSS‐SECTIONAL STUDIES
Bibliographic record
Abstract
The Montreal Cognitive Assessment (MoCA) is a cognitive screening test for the detection of cognitive impairment. The questions of MoCA cover multiple cognitive domains, but the interview with MoCA takes administration time from healthcare professionals. The objective of this study is to investigate the screening performance of different domains and possible shortened versions of MoCA. This study is a systematic review and meta-analysis. Cross-sectional studies using MoCA to assess cognitive impairment among patients with MCI or dementia were identified from OVID databases until Oct 2018. The questions from MoCA were categorized into seven domains, including memory, visuospatial abilities, language, attention, orientation, abstraction, and naming. The outcomes of this study were the changes in scores for the cognitive questions of MoCA between the patients with MCI or dementia and those with normal cognition. Standardized mean difference (SMD) with 95% confidence interval (95% CI) was used. SMDs were combined with meta-analysis. Risk of bias and quality of included studies were assessed. Subgroup analyses were conducted for different types of cognitive impairment. A total of 33 studies with 4,528 participants were included. The questions on memory and visuospatial abilities were more sensitive to detect MCI (SMD = −1.29, 95% CI = −1.47 to −1.11, and SMD = −1.14, 95% CI = −1.35 to −0.93, respectively), while the questions on naming was less sensitive (SMD = −0.33, 95% CI = −0.48 to -0.18). A shortened version of MoCA without questions on naming showed an identical diagnostic performance with full-MoCA in the detection of MCI (SMD = −2.22, 95% CI = −2.47 to −1.98, and SMD = −2.22, 95% CI = −2.47 to −1.97, respectively). Similar results were found in the detection of dementia. In subgroup analysis, the questions on orientation was more important to detect Alzheimer's disease.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.017 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".