Mortality risk in hemodialysis patients according to anemia control and erythropoietin dosing
Bibliographic record
Abstract
There is no consensus about the toxicity of erythropoiesis-stimulating agents among hemodialysis patients. We aimed to calculate the risk of death according to anemia control and erythropoietin (EPO) dosing among end-stage renal disease patients undergoing hemodialysis. We retrospectively studied 156 end-stage renal disease patients on hemodialysis from a single renal unit during 12 months. Participants were classified according to anemia control into four groups: excellent (A), good (B), moderate (C) and bad (D) control. They were also classified according to EPO dosing into two groups: usual and high EPO dosing. The Cox proportional hazards regression model, adjusted for the difference in age, sex, time on dialysis, comorbidity, albumin, and Kt/V index, was performed to calculate the risk of death according to anemia control and EPO dosing profiles. Multivariate analysis by backward stepwise logistic regression was used to calculate the risk of death according to the variables that differed in the comparison between survivors and nonsurvivors. The hazard ratio of death was not significant according to anemia control profile C/D vs. A/B, but hazard ratio was 2.967 (95% confidence interval [CI] = 1.132-7.777; P = 0.027) for high EPO dosing profile patients. The multivariate analysis showed comorbidity (odds ratio [OR] = 8.958; 95% CI = 2.843-26.223; P < 0.001], high EPO dosing profile (OR = 5.172; 95% CI = 1.663-16,081; P = 0.005), age (OR = 1.056; 95% CI = 1.020-1.094; P = 0.002), and mean hemoglobin (OR = 0.435; 95% CI = 0.267-0.709; P = 0.001) to be predictive of death. Even though we cannot conclude that mortality risk is due to EPO toxicity, hemodialysis patients using high EPO dosing must be seen as at risk.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".