Serum Erythropoietin Level and Mortality in Kidney Transplant Recipients
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Posttransplant anemia is frequently reported in kidney transplant recipients and is associated with worsened patient survival. Similar to high erythropoiesis-stimulating agent requirements, resistance to endogenous erythropoietin may be associated with worse clinical outcomes in patients with ESRD. We examined the association between serum erythropoietin levels and mortality among kidney transplant recipients. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: We collected sociodemographic, clinical, medical, and transplant history and laboratory data at baseline in 886 prevalent kidney transplant recipients (mean age 51 ± 13 [SD] years, 60% men, 21% diabetics). A solid-phase chemiluminescent immunometric assay was used to measure serum erythropoietin. Cox proportional hazards regression was used to model the association between baseline serum erythropoietin levels and all-cause mortality risk. RESULTS: During the median 39-month follow-up, 99 subjects died. The median serum erythropoietin level was 10.85 U/L and hemoglobin was 137 ± 16 g/L. Mortality rates were significantly higher in patients with higher erythropoietin levels (crude mortality rates in the highest to lowest erythropoietin tertiles were 51.7, 35.5, and 24.0 per 1000 patient-years, respectively [P = 0.008]). In unadjusted and also in adjusted Cox models each SD higher serum erythropoietin level significantly predicted all-cause mortality: HR(1SD increase) 1.22 and 1.28, respectively. In adjusted Cox models each SD higher serum erythropoietin/blood hemoglobin ratio also significantly predicted all-cause mortality: HR(1SD increase) 1.32. Serum erythropoietin predicted mortality in all analyzed subgroups. CONCLUSIONS: In this sample of prevalent kidney transplant recipients, higher serum erythropoietin levels were associated with increased mortality.
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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 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".