A faster decline of residual kidney function and erythropoietin stimulating agent hyporesponsiveness in incident hemodialysis patients
Bibliographic record
Abstract
Abstract Introduction Erythropoietin stimulating agents (ESA) hyporesposiveness has been associated with increased mortality in hemodialysis (HD) patients. However, the impact of decline of residual kidney function (RKF) on ESA hyporesposiveness has not been adequately elucidated among patients receiving HD. Methods The associations of RKF decline with erythropoietin resistance index (ERI; average weekly ESA dose [units])/post‐dialysis body weight [kg]/hemoglobin [g/dL]) were retrospectively examined across four strata of annual change in RKF (residual renal urea clearance [KRU] < −3.0, −3.0 to <−1.5, −1.5 to <0, ≥0 mL/min/1.73 m2 per year; urinary volume < −600, −600 to<−300, −300 to <0, ≥0 mL/day per year) using logistic regression models adjusted for clinical characteristics and laboratory variables in 5239 incident HD patients in a large US dialysis organization between 1 January 2007 and 31 December 2011. Findings The median values of the annual change in KRU and urinary volume were −1.2 (interquartile range [IQR]: −2.8 to 0.1) mL/min/1.73 m2 per year and −250 (IQR: −600 to 100) mL/day per year. A faster KRU decline in the first year of HD was associated with higher odds for ESA hyporesponsiveness: KRU decline of <−3.0, −3.0 to <−1.5, and −1.5 to <0/min/1.73 m2 per year were associated with adjusted odds ratios (OR) of 2.07 (95% confidence interval [CI]: 1.66–2.58), 1.54 (95%CI: 1.28–1.85), and 1.26 (95%CI: 1.07–1.49), respectively (reference: ≥0 mL/min/1.73 m2 per year). These associations were consistent across strata of baseline KRU, age, sex, race, diabetes, congestive heart failure, hemoglobin, and serum albumin. Sensitivity analyses using urinary volume as another index of RKF showed consistent associations. Discussion A faster RKF decline during the first year of dialysis was associated with ESA hyporesponsiveness and low hemoglobin levels among incident HD 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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".