VALIDATION OF THE ALTERNATIVE BRAZILIAN VERSION OF THE MONTREAL COGNITIVE ASSESSMENT (MOCA-BR): PILOT STUDY
Bibliographic record
Abstract
Background: The current version of the Brazilian Montreal Cognitive Assessment (MoCA-BR) did not have a reliable cross-cultural adaptation to Brazil-ian Portuguese. In previous stages of this study, the Alternative Version of the MoCA-BR was developed, with changes in the sections: Memory and Delayed Recall, Language and Naming. Objectives: to verify the influence of crosscultural adaptation on the performance of cognitive tools, and the accuracy of the Alternative Version of the MoCA-BR. Methods: a pilot, prospective, longitudinal and analytical study. Both versions of the test were applied in a randomized and cognitively healthy population, between 18 and 60 years, within a medium interval of 54,56 days between the questionnaires. Results: out of 104 participants, 70 were included (64.3% female, 40.2 years). The alternative version obtained superior performances in the naming domain (p < 0.001), and in the adapted sentence in the language domain (p = 0.003). There was no significant difference in the delayed recall domain. The alternative version showed good internal consistency, with a Cronbach’s alpha of 0.75. The cutoff point suggested by the study is 27 points, with sensitivity and specificity of 91.3% and 79.2%, respectively. Conclusions: Cultural factors affect the accuracy of cognitive tests, and adaptation is essential for their use in different countries.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".