Critical appraisal of randomized controlled trials of anemia correction in patients with renal failure
Bibliographic record
Abstract
PURPOSE OF REVIEW: Recent randomized controlled trials (RCTs) have reported risks and benefits in the correction of anemia using erythropoietin-stimulating agents (ESAs) in patients with predialysis chronic kidney disease (CKD) or end-stage renal disease. The purpose of this review is to critically appraise the RCTs most relevant to clinical decision-making. RECENT FINDINGS: The Trial to Reduce cardiovascular Events with Aransep Therapy (TREAT) is the biggest and best designed RCT performed so far, enrolling 4038 diabetic patients with CKD in a double-blind, placebo-controlled trial. Comparing the high hemoglobin (Hb) darbopoietin group with placebo, it reported no difference in cardiac and renal outcomes, a significantly increased risk of stroke, and modest improvement in fatigue and transfusion rate. Meta-analysis demonstrated that correction of anemia with ESA was associated with a significantly increased risk of hypertension and vascular access clotting and an increased risk of death that approached statistical significance. Quality-of-life improvements appeared to maximize in the target Hb range of 10-12 g/dl. SUMMARY: Treatment of renal anemia using ESAs to target Hb higher than 13 g/dl is harmful. Treatment of Hb below 9 g/dl provides substantial transfusion and quality-of-life benefits, but safety is unknown. Target Hb levels of 10-12 g/dl seem reasonable, but increasing ESA doses in hyporesponsive patients to achieve a specific target is not.
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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 | 0.052 | 0.195 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.015 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".