Obesity and survival in a national cohort of incident hemodialysis patients: An analysis of the Brazilian Dialysis Registry
Bibliographic record
Abstract
Abstract Introduction A phenomenon called the “obesity paradox” has consistently been reported in several cohorts of patients on chronic hemodialysis. In this setting, a higher body mass index (BMI) is paradoxically associated with better survival. This study aimed to evaluate the effect of BMI on mortality in patients undergoing chronic hemodialysis using the Brazilian Dialysis Registry. Methods This was a retrospective national cohort study with data on incident hemodialysis patients collected between January 2011 to December 2018. Those aged <18 or > 80 years were excluded from the study. The variables studied were the clinical and laboratory data regularly collected at the dialysis units. The variable of primary interest was BMI, represented as the median of the entire dialysis treatment and stratified into four ranges according to the World Health Organization (WHO) classification. The primary outcome was death within 4 years. Cox proportional hazards regression analysis was used to test associations with mortality. Findings The analyzed sample consisted of 5489 patients from 73 centers in five regions of the country. Of these, 5.9% were underweight, 48.3% were of normal weight, 31.0% were overweight, and 14.7% were obese. The 4‐year survival rates in these BMI ranges were 58%, 70%, 75%, and 80%, respectively. The probability of survival for each BMI extract was significantly different from that in the normal‐weight range (p < 0.05). In the fully adjusted Cox proportional hazard regression model, BMI > 24.9 kg/m2 remained an independent protective factor for mortality (HR: 0.76, 95% CI: 0.62–0.95, p = 0.016). Discussion In Brazil, being overweight and obese are protective factors for survival in the chronic hemodialysis population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".