Meta-analysis of generalization reliability of the Montreal Cognitive Assessment (MoCA) questionnaire in cognitive impairment
Bibliographic record
Abstract
Introduction Dementia is a syndrome of high prevalence and health impact. The Montreal Cognitive Assessment (MoCA) questionnaire is a screening tool whose use has increased in recent years, especially in cases of mild cognitive impairment. Some studies suggest that its ability to detect cognitive impairment, especially in early or mild stages, seems to be greater than gold-standard instruments (Ciesielska et al., 2016). Objectives We have performed a meta-analysis of reliability generalization to see if different adaptations and use in different contexts show consistent results. Methods We performed a literature search in PyscINFO and Medline with the terms “Cognitive impairment” AND “internal consistency” AND “Cronbach”, using the following inclusion criteria: 1. Be a study in which the MoCA scale was applied to a population sample. 2. Studies published in the last 10 years. 3. Studies that provide the reliability coefficient or sufficient data to calculate them. 4. Be written in English or Spanish. We have limited our study to the last 10 years and the English language has given us a total of 19 results in Medline and 132 results in PsycINFO. Subsequently, we completed this search by snowball sampling. A random effects model was assumed for the statistical calculations and the transformation of our values using the Hakstian and Whalen (1976) proposal. Statistical analysis was performed with the MAJOR package of the Janovi program, based on the R environment. Results We obtained a mean reliability for the transformed test scores of 0.42 (95% CI: 0.38 - 0.45), as well as high heterogeneity measured by Cochran’s Q statistic and the I2 index, which is attributed after analysis of moderating variables to the geographical adaptation of the questionnaire and the type of patient on whom it is applied. Our Funnel Plot graph indicates that we do not appear to have committed a publication bias. Conclusions Our meta-analysis shows high heterogeneity, mainly explained by the population of origin, both geographically (continent) and clinically (presence of primary cognitive impairment or not), with special incidence in those with impairment secondary to other pathologies, mainly neurological. However, we should consider the high probability that we have not included important variables in our analysis that could increase the explanatory power of our model. Disclosure of Interest None Declared
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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 | 0.038 | 0.081 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.055 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".