Factors Affecting Perioperative Transfusion Decisions in Patients with Coronary Artery Disease Undergoing Coronary Artery Bypass Surgery
Bibliographic record
Abstract
BACKGROUND: A high proportion of patients having cardiac bypass surgery receive erythrocyte transfusions. Decisions about when to transfuse patients having surgery for coronary artery disease may impact on erythrocyte utilization and patient morbidity and mortality. There are no published data about the factors that influence physicians' decisions to transfuse erythrocytes to these patients. The objectives of this study were to determine the hemoglobin concentration for transfusion and the factors that influence physicians' perioperative transfusion decisions for coronary artery bypass patients. METHODS: The authors conducted a cross-sectional study using pretested, self-administered, mailed questionnaires sent in 2004 to all cardiac surgeons and anesthesiologists in Canada who participate in coronary artery bypass surgery. The questionnaire included four intraoperative and four postoperative vignettes. Factors assessed included patient age, sex, cardiac index, and myocardial ischemia. RESULTS: The response rates were 70% (345 of 489) for the intraoperative and 61% (297 of 489) for the postoperative case scenarios. The mean hemoglobin concentrations for transfusion were 7.0 g/dl for the intraoperative case scenarios and 7.2 g/dl for the postoperative case scenarios. Older age, the presence of myocardial ischemia, and a low cardiac index were factors that increased the hemoglobin concentration for transfusion (P < 0.0001). Physicians ranked myocardial ischemia as the most significant factor affecting their transfusion decisions. CONCLUSIONS: Factors such as the presence of a low cardiac index, myocardial ischemia, and older age increase the hemoglobin concentrations at which physicians transfuse coronary bypass surgery patients. Future studies are required to elucidate whether transfusions based on these variables affect patient morbidity and mortality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".