The greatly misunderstood erythropoietin resistance index and the case for a new responsiveness measure
Bibliographic record
Abstract
Introduction The optimal use of erythropoiesis stimulating agents (ESAs) to treat anemia in end stage renal disease remains controversial due to reported associations with adverse events. In analyzing these associations, studies often utilize ESA resistance indices (ERIs), to characterize a patient's response to ESA. In this study, we examine whether ERI is an adequate measure of ESA resistance. Methods We used retrospective data from a nonconcurrent cohort study of incident hemodialysis patients in the United States (n = 9386). ERI is defined as average weekly erythropoietin (EPO) dose per kg body weight (wt) per average hemoglobin (Hgb), over a 3-month period (ERI = (EPO/wt)/Hgb). Linear regression was used to demonstrate the relationship between ERI and weight-adjusted EPO. The coefficient of variation was used to compare the variability of Hgb with that of weight-adjusted EPO to explain this relationship. This analysis was done for each quarter during the first year of dialysis. Findings ERI is strongly linearly related with weight-adjusted EPO dose in each of the four quarters by the equation ERI = 0.0899*(EPO/wt) (range of R(2) = 0.97-0.98) and weakly linearly related to 1/Hgb (range of R(2) = 0.06-0.16). These correlations hold independent of age, sex, hgb level, ERI level, and epo-naïve stratifications. Discussion ERI is strongly linearly related to weight-adjusted (and nonweight-adjusted) EPO dose by a "universal," not patient-specific formula, and thus is a surrogate of EPO dose. Therefore, associations between ERI and clinical outcomes are associations between a confounded EPO dose and those outcomes.
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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 | 0.137 | 0.283 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.006 | 0.017 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".