P1‐471: Tutoral issues in Brazilian version of MoCA test
Bibliographic record
Abstract
One important issue in dementias is the characterization of the cognitive impairment in its initial phase. In this context, the concept of mild cognitive impairment (MCI) emerged. MCI is typically related to the intermediate clinical state between normal cognitive aging and dementia, and it precedes dementia in many cases based on the fact that no adequate screening tests were available to detect MCI, the Montreal Cognitive Assessment (MoCA) was developed as a tool to screen patients who present with mild cognitive complaints and usually perform in the normal range on the MMSE. Considering the great importance and utility of this test, we decided to translate it to Brazilian Portuguese and use it in our population. Once we already had the Brazilian Portuguese version of MoCA we have tested 80 subjects. In this first moment, we didnot proceed to the transcultural adaptation and the sub items were kept as close to the original as possible. An analysis of our MCI subjects with 12 years or over of education showed a performance similar to that of the original investigation but, of course, such a small sample is not statistically reliable . Trail making was pointed as difficult by our population as well as sentence repetition and naming a rhino. Modifying this sub -items maybe could make the test more consistent To improve the test there could be modifications in some sub-items, so that it could be easier for less educated subjects, at the cost of losing sensitivity for the higher educated. Based on this facts, our version should be revised to improvements, so we can increase the value of Cronbach alpha, thus improving its internal consistency, which will allow us in continuing this work validate it for use in Brazilian population.
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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 | 0.004 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".