Combined erythropoietin and iron therapy for anaemic patients undergoing transcatheter aortic valve implantation: the EPICURE randomised clinical trial
Bibliographic record
Abstract
AIMS: The aim of this study was to evaluate, in anaemic patients, the efficacy of erythropoietin (EPO) in reducing red cell (RC) transfusion rates post TAVI. METHODS AND RESULTS: This was a randomised double-blind trial. Patients with severe symptomatic aortic stenosis and concomitant anaemia with an indication for TAVI were randomised (1:1) to receive two weight-based doses of EPO (darbepoetin alfa)+iron or placebo at days 10 (±4 days) and 1 (±1 day) pre TAVI. The primary outcome was the rate of RC transfusions at 30 days. A total of 100 patients (mean age 81±7 years, male 49%) were included: 48 patients received EPO (+iron) and 52 patients received placebo. Baseline characteristics and procedural findings were well balanced between groups except for baseline haemoglobin levels, which were lower in those patients receiving EPO (10.7±1.2 vs. 11.3±1.1 g/dl, p=0.01). The rate of 30-day RC transfusion was similar in both groups (27.1 vs. 25.0% in the EPO and placebo groups, respectively; adjusted odds ratio 1.05, 95% CI: 0.42-2.64, p=0.92), and no differences were observed in the number of RC units per transfused patient (1 [1-3] vs. 2 [1-2] in the EPO and placebo groups, respectively, adjusted p=0.99). Rates of 30-day mortality, stroke, new-onset atrial fibrillation, acute kidney injury, and troponin peak were also similar between groups (p>0.20 for all). CONCLUSIONS: EPO (+iron) administration failed to reduce RC transfusion rates or the per-patient number of transfusion units in anaemic patients undergoing TAVI.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".