Prevalence and factors associated with frailty among incident kidney transplant patients: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Evidence on frailty prevalence in Brazilian patients with kidney transplant (KT) is scarce. OBJECTIVES: To estimate frailty prevalence in pre-KT patients and its association with functional, cognitive, and laboratory anomalies. DESIGN AND SETTING: Cross-sectional descriptive study included adult KT candidates assessed within 24 hours before KT, at two medical centers in Northeast Brazil. METHODS: Frailty was classified as non-frail (scores 0-1), intermediate frail (score 2), or frail (scores 3-5), using Fried et al. criteria. Patients were divided into: Non-frail (0-1) and Frail (≥ 2) groups. Katz and Lawton's scales assessed the dependence on basic (ADLs) and instrumental (IADLs) activities of daily living, respectively. Montreal Cognitive Assessment was used to evaluate cognition. Laboratory tests were performed during pre-KT evaluation. RESULTS: Among 82 patients, most were male (80.5%), mixed-race (76.8%), and 48.8 ± 14.9-years-old. The Frail group (63.4%) comprised 34.1% intermediate frail, and 29.3% frail individuals. This group had a higher prevalence of hypertension (90.4% vs. 70%, P = 0.018), rheumatological diseases (15.4% vs. 0%, P = 0.024), cognitive impairment (71.0% vs. 40.7%, P = 0.020), dependence on ADLs (32% vs. 0%, P < 0.001) and IADLs (82% vs. 56.7%, P = 0.014), lower hemoglobin (11.9 ± 2.7 g/dL vs. 13.4 ± 1.8 g/d, P = 0.005), and lower creatinine levels (7.1 mg/dL, IQR 6-10 vs. 9.1 mg/dL, IQR 7-11, P = 0.044). CONCLUSIONS: Pretransplant frailty was prevalent and associated with functional disability, cognitive impairment, and biomarkers indicating sarcopenia. Early frailty assessment and identification of modifiable factors are essential.
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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 | 0.000 |
| 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".