Restrictive versus liberal transfusion in patients with diabetes undergoing cardiac surgery: An o <scp>pen‐label,</scp> randomized, blinded outcome evaluation trial
Bibliographic record
Abstract
Abstract Aim To characterize the association between diabetes and transfusion and clinical outcomes in cardiac surgery, and to evaluate whether restrictive transfusion thresholds are harmful in these patients. Materials and Methods The multinational, open‐label, randomized controlled TRICS‐III trial assessed a restrictive transfusion strategy (haemoglobin [Hb] transfusion threshold <75 g/L) compared with a liberal strategy (Hb <95 g/L for operating room or intensive care unit; or <85 g/L for ward) in patients undergoing cardiac surgery on cardiopulmonary bypass with a moderate‐to‐high risk of death (EuroSCORE ≥6). Diabetes status was collected preoperatively. The primary composite outcome was all‐cause death, stroke, myocardial infarction, and new‐onset renal failure requiring dialysis at 6 months. Secondary outcomes included components of the composite outcome at 6 months, and transfusion and clinical outcomes at 28 days. Results Of the 5092 patients analysed, 1396 (27.4%) had diabetes (restrictive, n = 679; liberal, n = 717). Patients with diabetes had more cardiovascular disease than patients without diabetes. Neither the presence of diabetes (OR [95% CI] 1.10 [0.93‐1.31]) nor the restrictive strategy increased the risk for the primary composite outcome (diabetes OR [95% CI] 1.04 [0.68‐1.59] vs. no diabetes OR 1.02 [0.85‐1.22]; P interaction = .92). In patients with versus without diabetes, a restrictive transfusion strategy was more effective at reducing red blood cell transfusion (diabetes OR [95% CI] 0.28 [0.21‐0.36]; no diabetes OR [95% CI] 0.40 [0.35‐0.47]; P interaction = .04). Conclusions The presence of diabetes did not modify the effect of a restrictive transfusion strategy on the primary composite outcome, but improved its efficacy on red cell transfusion. Restrictive transfusion triggers are safe and effective in patients with diabetes undergoing cardiac surgery.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".