Translation and validation of the audiovisual version of the Montreal cognitive assessment in older adults in Brazil
Bibliographic record
Abstract
BACKGROUND: The use of a reliable remote cognitive screening test for older adults is crucial for the diagnosis of cognitive impairment. This study aimed to translate and validate the audiovisual Montreal Cognitive Assessment (MoCA)for older adults in Brazil. METHODS: One hundred and fourteen older adults were recruited from the community and demographic, functional, mood, and cognitive data were collected. Participants were classified into two groups: cognitively healthy or mild cognitive impairment (MCI). Statistical analyses were performed in order to assess the validity of the test and the cutoff score. RESULTS: The psychometric properties of the audiovisual MoCA showed good convergent validity. The audiovisual MoCA was represented as a unifactorial adjusted model, the composite reliability value was acceptable and a cutoff point of ≥23 reached adequate sensitivity and specificity at 0.77 and 0.92, respectively. CONCLUSIONS: The translated audiovisual MoCA is a valid and reliable cognitive screening test that can be administered remotely in older adults in Brazil. The test demonstrated a great ability to discriminate older adults with MCI from cognitively healthy adults. Future studies should focus on validating the audiovisual MoCA using other target population groups in order to expand the use of this remote screening test.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".