Impact of body mass index on short‐term and long‐term survival in prevalent hemodialysis patients
Bibliographic record
Abstract
INTRODUCTION: Numerous studies showed that higher body mass index (BMI) is associated with better survival in hemodialysis (HD) patients. Most of them evaluated short-term mortality. It has been suggested that presence of inflammation may be a key modifier of relationship between BMI and mortality in incident HD patients. We examined whether presence of inflammation modifies the association between BMI and mortality in both short-term and long-term follow-up in a large group of prevalent HD patients. METHODS: ). Inflammation status was defined as present (inflamed) (C-reactive protein (CRP) ≥1.0 mg/dL and/or serum albumin ≤3.5 g/dL) or absent (noninflamed). FINDINGS: During 7 years of follow-up 1386 patients (42.6%) died. Compared to noninflamed patients, inflamed patients in the lowest BMI quartile showed 5-fold increased risk for mortality in the short-term (95% confidence interval [CI] 2.82-9.22, P < 0.001) and 3-fold in the long-term (95%CI 2.42-4.27, P < 0.001) compared to the highest BMI quartile. Whereas, inflamed patients in the highest BMI quartile experienced 2-fold increased risk in short-term (95%CI 1.17-3.74, P = 0.01) and 1.68-fold increased risk in long-term (95%CI 1.30-2.18, P < 0.001) than in noninflamed patients. The protective effect of BMI for overall mortality was present in all age groups, in both genders, in patient with and without diabetes. BMI was not a mortality predictor in patients with HD duration more than 76 months at baseline. The protective effect of BMI was observed in all albumin tertiles. In patients in the lowest CRP tertile, BMI was not associated with mortality. DISCUSSION: Higher BMI is associated with lower short-term and long-term mortality risk, especially in patients with inflammation in a prevalent HD population.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".