Evaluation of adult and elderly performance in MoCA and verbal fluency test in Recife-PE
Bibliographic record
Abstract
Background: Considering the increase in life expectancy, the use of screening tests contributes to the detection of cognitive decline. However, different socioeconomic conditions can influence the performance of individuals. Objectives: To compare the performance of participants in the Montreal Cognitive Assessment (MoCA) and the Verbal Fluency Test (VFT), assessing the influence of schooling on the score. Design and setting: Cross-sectional, descriptive study, with 21 participants without cognitive complaints, between January and April 2021, in Recife, Pernambuco. Methods: Data were analyzed through SPSS software, Shapiro-Wilk test and Pearson’s correlation coefficient (PCC). Results: In all, 21 MoCA tests were applied (Average score: 19.8 points; SD = ± 4.3). The population has a mean age of 56.2 years (SD = ± 10.2), education of 11.8 years (SD = ± 3.5), and a predominance of females (93.5%). In the VFT, the total average performance resulted in 11.5 words (SD = ± 5.1), the first interval, 6.1 (SD = ± 2.5), the second, 2.8 (SD = ± 1, 9) and the third, 2.1 (SD = ± 2.1). There was a correlation between the performance in MoCA and VFT (PCC = 0.717; P = 0.01), and between performance and years of schooling (MoCA: PCC = 0.688; P = 0.01 vs VFT: PCC = 0.489; P = 0.02). Conclusions: Both tools were correlated with the participant’s level of education. However, VFT obtained a lower correlation
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".