PERFORMANCE IN COGNITIVE TESTS AND SUBJECTIVE PERCEPTION OF THE MEMORY IN INDIVIDUALS FROM RECIFE-PE
Bibliographic record
Abstract
Introduction: Currently, there is a discussion about how subjective memory perception can predict performance in cognitive tests. Objective: To correlate the subjective perception of memory with performance in cognitive tests Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA). Methods: Cross-sectional, descriptive study with 28 participants without cognitive complaints. People aged 40 years or more and at least four years of schooling were included. Participants were asked about their subjective perception of memory and then submitted to cognitive tests. Data were analyzed using SPSS software (v.23). Results: The population consisted of female individuals (89.3%), a mean age of 58.9 years (SD=±9.6), education of 11.9 years (SD=±4.4). As for the perception of memory, 53,5% of the individuals classified it as neutral, of which 7.1% had a score greater than or equal to 26 points in the MoCA, while 57.1% scored 24 or more in the MMSE. Also, 43,5% rated memory positively and, among them, 92.9% scored well on the MMSE, while only 28.7% had good performance on the MoCA. This self-assessment was correlated with MoCA performance (χ2=10.38; p=0.001). Conclusion: The subjective perception of memory was correlated with the performance of participants in the MoCA. Individuals with good perceptions had predominantly low performance on the tool.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".