COGNITIVE ASSESSMENT OF ADULTS AND ELDERLY IN RECIFE-PE
Bibliographic record
Abstract
Background: Currently, the influence of schooling on the assessment parameters of cognitive tests is under debate. Objectives: Evaluate the performance of possessors in the MMSE (Mini-mental State Examination) and MoCA (Montreal Cognitive Assessment), evaluating the influence of education on the performance of the participants. Methods: This is a cross-sectional, descriptive study with 33 participants without cognitive complaints. These were people aged 40 years or more, and at least four years of schooling. Data were formed in SPSS (v.23), using Spearman’s correlation coefficient (CS). Results: The population is predominantly composed of women (87.8%), with a mean age of 58 years (SD = ± 9.5), and education of 11.7 years (SD = ± 4.2 years). The median performance on the MMSE was 25 points (95%CI = 24.5-26.4) and, of these, 75.8% had a value equal to or greater than 24 points. At the same time, the median score in the MoCA was 20 points (95%CI = 18.6-21.7) and 18.2% of those evaluated scored equal to or greater than 26 points. As for the influence of education, there was a correlation for both tools (MMSE: CS = 0.457; p = 0.008; MoCA: CS = 0.556; p = 0.001). Regarding age, there was a correlation with MMSE performance (CS = 0.368; p = 0.035). Conclusion: MoCA and MEEM are correlated with the length of formal education. Thus, it is important to consider this factor when interpreting these scales.
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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 | 0.001 | 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".