Concordance, reproducibility, and usability of a Brazilian version of the Montreal Cognitive Assessment (MoCA) questionnaire in electronic format (Appsheet) to screen cognitive impairment in older women
Bibliographic record
Abstract
To analyze the agreement between instrument versions (paper vs. digital) of the MONTREAL COGNITIVE ASSESSMENT (MoCA), and evaluate the reproducibility and usability of the electronic instrument. A total of 118 community-dwelling older women, aged 60 to 79 years, participated in a two-stage data collection process. In the first stage, both the paper and digital versions of the MoCA were randomly administered. Two weeks later, a subset of the participants was randomly selected for a retest, performed only on the mobile phone. Data analysis included agreement coefficients, Cronbach's alpha for internal consistency, mean square differences, mean, and standard deviation to screen for mild cognitive impairment. The results revealed substantial agreement (CCC=0.777; p < 0.001) and moderate to high internal consistency (α = 0.736). High reproducibility was observed across different age ranges, 60 to 69 years (CCC=0.752; p < 0.001) and 70 to 79 years (CCC = 0.772; p < 0.001), and usability was rated as excellent (76.25). These findings provide evidence that the electronic version of the MoCA (DS-MoCA) using a mobile phone is a valid alternative for cognitive assessment in older women.
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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 | 0.023 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".