TRANSCULTURAL ADAPTATION DESIGN OF MONTREAL COGNITIVE ASSESSMENT (MOCA) IN BRAZIL
Bibliographic record
Abstract
Introduction: Montreal Cognitive Assessment (MoCA) is the most common cognitive screening instrument for Mild Cognitive Impairment detection. Although the current Brazilian version (MoCA-BR) has been validated, in clinical practice, it is observed that adults with normal cognitive function, especially those less educated, rarely reaches the maximum score of 30 points on the test. Objective: Introduce a methodology to adjust the Brazilian version according to the Brazilian culture. A cross-se Methods: ctional observational study was conducted with 294 participants. In the Memory section, we used the free listing technique to replace words. In the Naming section, an epidemiological survey of the most pinpointed gures was conducted. Replication of Sentence section was modied based on meetings between researchers and Portuguese teachers uent in English. The alternative version of MoCA-BR was composed by: "az Results: ul" (blue), "braço" (arm), "orquídea" (orchid), "seda" (silk) and “igreja” (church) in Memory Section; giraffe, elephant, and lion in the Naming section; “Eu só sei que é João quem será ajudado hoje” and "O gato sempre se esconde embaixo do sofá quando o cachorro está na sala" in the Replication of Sentence section. Our Conclusions: data reinforce the need to adapt the MoCA-BR. We present an alternative version of MoCA-BR, which contemplates the linguistic and cultural requirements of the transcultural adaptation process. The next step is to apply this version to obtain its validation. We believe that this adaptation may allow a future better applicability of the MoCA-BR, especially in less educated people, without underestimating the scores of cognitively normal individuals
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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 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".