Relationship between erythropoietin resistance index and left ventricular mass and function and cardiovascular events in patients on chronic hemodialysis
Bibliographic record
Abstract
The response to erythropoietin (EPO) treatment varies considerably in individual patients on chronic hemodialysis. The EPO resistance index (ERI) has been considered useful to assess the EPO resistance and can be easily calculated in the clinic. The aim of this study was to investigate the association between ERI and left ventricular mass (LVM) and function and to determine whether ERI was associated with cardiovascular events in patients on hemodialysis. This study was designed prospectively. Clinical, laboratory, and echocardiographic variables were assessed in 72 patients on hemodialysis. The ERI was determined as the weekly weight-adjusted dose of EPO (U/kg/week) divided by hemoglobin concentration (g/dL). Patients were divided into three groups by tertiles of ERI. Patients with higher tertiles of ERI had a higher LVM index and lower LV ejection fraction compared with those with lower tertiles of ERI (P = 0.019 and P = 0.030, respectively). The median follow-up period was 53 months. The Kaplan-Meier plot showed increased frequency of cardiovascular events in patients with higher tertiles of ERI, compared with those with lower tertiles of ERI (P = 0.011, log-rank test). The multivariate Cox proportional hazard models showed that the ERI was the significant independent predictor of cardiovascular events (HR 3.00, 95% CI, 1.04-8.62, P = 0.042). Our data show that ERI was related with LVM index, LV systolic function and cardiovascular events in patients with hemodialysis. By monitoring of ERI, early identification of the EPO resistance may be helpful to predict the cardiovascular risk in hemodialysis patients.
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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 | 0.000 |
| 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 | 0.001 | 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".