Factors associated with frailty in older users of Primary Health Care services from a city in the Brazilian Amazon
Bibliographic record
Abstract
Abstract Objective To estimate the prevalence of frailty syndrome and its association with socioeconomic, demographic and health variables, in elderly people treated at two Health Units in the city of Rio Branco, Acre, from October 2016 to June 2017. Method The prevalence of frailty was measured using the Edmonton Frail Scale (EFS), and associations were tested with selected variables. Poisson regression, with robust variance and 95% confidence intervals, was used to estimate the prevalence ratios and define the adjusted model. All analyzes took into account the sample weights and were performed using SPSS version 20. Results It was found that 35.1% of the sample showed fragility. The prevalence of frailty was associated with being 75 years old or more, physical inactivity, nutritional risk, cognitive deficit, negative health perception, using 5 or more medications and having/history of cancer, falls in past year, living alone, unsatisfactory neighborhood safety and being of ethnicity/non-white color. Conclusion The alert profile for screening for frailty was verified, which may assist in the clinical practice of FHS professionals in the study population, and also considers the need to implement and strengthen eldely's health care programs and performance of the Family Health Support Centers.
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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 | 0.000 | 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 | 0.001 |
| 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".