Erythropoietin in Acute Kidney Injury (EAKI): a Pragmatic Randomized Clinical Trial
Bibliographic record
Abstract
Abstract Background Treatment with erythropoietin is well established for anemia in chronic kidney disease patients but not well studied in acute kidney injury.MethodsThis is a multicenter, randomized, pragmatic controlled clinical trial. It included 134 hospitalized patients with anemia defined as hemoglobin <11 g/dL and acute kidney injury defined as an increase of serum creatinine of 0.3 mg/dL within 48 hours or 1.5 times baseline. One arm received recombinant human erythropoietin 4000 UI subcutaneously every other day (intervention; n=67) and the second received standard of care (control; n=67) during the hospitalization until discharge or death. The primary outcome was the need for transfusion; secondary outcomes were death, renal recovery, need for dialysis.ResultsThere was no statistically significant difference in transfusion need (RR=1.05, 95%CI 0.65,1.68; p=0.855), in renal recovery full or partial (RR=0.96, 95%CI 0.81,1.15; p=0.671), in need for dialysis (RR=11.00, 95%CI 0.62, 195.08; p=0.102) or in death (RR=1.43, 95%CI 0.58,3.53; p=0.440) between the erythropoietin and the control group. ConclusionsErythropoietin treatment had no impact on transfusions, renal recovery or mortality in acute kidney injury patients with anemia. The trial was registered on ClinicalTrials.gov (NCT03401710, 17/01/2018).
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| 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".