Platelet transfusions during coronary artery bypass graft surgery are associated with serious adverse outcomes
Bibliographic record
Abstract
BACKGROUND: Platelet (PLT) transfusions are administered in cardiac surgery to prevent or treat bleeding, despite appreciation of the risks of blood component transfusion. The current analysis investigates the hypothesis that PLT transfusion is associated with adverse outcomes associated with coronary artery bypass graft surgery (CABG). STUDY DESIGN AND METHODS: Data originally collected during double-blind placebo-controlled phase III trials for licensure of Trasylol (aprotinin injection) were retrospectively analyzed. Adverse outcome data of patients (n = 1720) that received, and did not receive, perioperative PLT transfusion were compared. Logistic regression analysis was used to assess the association of perioperative adverse events with PLT transfusion. Propensity scoring analysis was used to verify results of the logistic regression. RESULTS: Patients receiving PLTs were more likely to have prolonged hospital stays, longer surgeries, more bleeding, re-operation for bleeding, and more RBC transfusions, and less likely to have full-dose aprotinin administration. Adverse events were statistically more frequent in patients that received one or more PLT transfusion. Logistic regression analysis showed that PLT transfusion was associated with infection, vasopressor use, respiratory medication use, stroke, and death. Propensity scoring analysis confirmed the risk of PLT transfusion. CONCLUSIONS: PLT transfusion in the perioperative period of CABG was associated with increased risk for serious adverse events. PLT transfusion may be a surrogate marker for sicker patients and have no causal role in the outcomes observed. However, a direct contribution to outcomes remains possible.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".