Race and Mortality in Hemodialysis Patients in Brazil
Bibliographic record
Abstract
Rationale & Objective: Studies in the United States and United Kingdom generally report better survival for Black than White patients undergoing maintenance hemodialysis, a finding not explained by differences in sociodemographics or comorbid conditions. It is not clear if such findings can be generalized to other countries. We investigated the association between race and mortality among a Black, White, and Mixed-Race sample of maintenance hemodialysis patients in Salvador, Brazil. Study Design: Prospective cohort study. Baseline data collection from July 1, 2005 through December 31, 2010. The follow-up period ended on December 31, 2017. Setting & Participants: The Prospective Study of the Prognosis of Chronic Hemodialysis Patients (PROHEMO) is a cohort of 1,501 patients from 4 dialysis units in Salvador, Brazil. Predictor: Race categorized as White (12.9%), Mixed-Race (62.4%), and Black (24.8%), using White as the reference category. Outcome: Survival. Analytical Approach: Using Cox regression models, we tested the association between race and mortality, with adjustments for age, sex, social factors, laboratory results, and comorbid conditions. Results: The mean age was 49 years for Black and Mixed-Race patients and 55 years for White patients. In a Cox model adjusted for age, mortality did not differ between Black and White patients (HR, 1.10; 95% CI, 0.66-1.83) or between Mixed-Race and White patients (HR, 1.00; 95% CI, 0.65-1.54). Adjustment for sociodemographics and comorbid conditions had minimal impact on these results. Limitations: Potential residual confounding and lack of adjustment for time-varying variables. Conclusions: Contrary to studies in the United States and United Kingdom, we did not find racial difference in mortality among patients in our Brazilian setting who were being treated by maintenance hemodialysis. These results underscore the importance of investigating racial differences in mortality among patients undergoing maintenance hemodialysis in different populations and countries.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".