Fatores associados à fragilidade de idosos com doença renal crônica em hemodiálise
Bibliographic record
Abstract
The scope of this article is to identify sociodemographic and clinical factors associated with the frailty of elderly people with chronic kidney disease on hemodialysis. This involved a correlational, cross-sectional study conducted in a dialysis center in the state of São Paulo. The sample consisted of 60 participants. The Participant Characterization Instrument was used for extracting sociodemographic and clinical data and the Edmonton Frail Scale was used to evaluate the level of frailty. Multivariate logistic regression was used to identify the factors associated with frailty. The mean age of the 60 patients included was 71.1 (± 6.9) years, predominantly male (70%), of which 36.7% were classified as frail. With respect to the factors associated with frailty among the variables of gender, age, self-reported skin color, schooling, monthly per capita income, hemodialysis time, number of associated diseases, falls in the year, hematocrit level, parathyroid hormone and use of calcitriol, it was found that only the monthly per capita income was significantly associated with frailty (OR = 0.44; 95% CI 0.1-0.9; p = 0.04). There was an association between frailty and income, showing that the elderly most at risk of frailty were those with lower income.
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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 | 0.003 |
| 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 | 0.000 | 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".