Frailty and cognitive performance of elderly in the context of social vulnerability
Bibliographic record
Abstract
Abstract Elderly who live in the context of social vulnerability have lower education and socioeconomic status. Objective: To analyze cognitive performance as a factor associated with frailty status in elderly living in contexts of social vulnerability. Methods: An exploratory, comparative, cross-sectional study using a quantitative method was conducted with elderly people registered at Social Assistance Reference Centers. A semi-structured interview, the Edmonton Frail Scale and Montreal Cognitive Assessment were applied. The project was approved by the Research Ethics Committee. To analyze the data, a logistic regression was performed considering two groups (frail and non-frail). Results: 247 elderly individuals participated in the study, with a mean age of 68.52 (±SD =7.28) years and education of 1-4 years (n=133). All the elderly evaluated resided in vulnerable regions. Regarding frailty, 91 (36.8%) showed frailty at some level (mild, moderate or severe) and 216 (87.4%) had cognitive impairment. On the regression analysis, frailty was associated with number of diseases (OR:1.60; 95%CI: 1.28-1.99) and cognition (OR:0.93; 95%CI: 0.89-0.98). Conclusion: Identifying level of frailty and cognition in socially vulnerable elderly reinforces the need for early detection in both these conditions by the public services that provide care for this population with a focus on prevention.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".