Influence of Erythrocyte Transfusion on the Risk of Acute Kidney Injury after Cardiac Surgery Differs in Anemic and Nonanemic Patients
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Bibliographic record
Abstract
BACKGROUND: Acute kidney injury (AKI) after cardiac surgery is a major health issue. Two important risk factors for AKI are preoperative anemia and perioperative erythrocyte transfusion, and elucidating their relationship may help in devising preventive strategies. METHODS: In this cohort study of 12,388 adults who underwent cardiac surgery with cardiopulmonary bypass and received three units or less of erythrocytes on the day of surgery, the authors used propensity score methods and conditional logistic regression to explore the relationship between preoperative anemia (hemoglobin less than 12.5 g/dL), erythrocyte transfusion on the day of surgery, and AKI (more than 50% decrease in estimated glomerular filtration rate from preoperative to postoperative day 3-4). RESULTS: AKI occurred in 4.1% of anemic patients (n = 94/2,287) and 1.6% of nonanemic patients (n = 162 of 10,101) (P < 0.0001). In the 2,113 propensity-score matched pairs, anemic patients had higher AKI rates than nonanemic patients (3.8% vs. 2.0%; P = 0.0007). AKI rates increased in direct proportion to the amount of erythrocytes transfused, and this increase was more pronounced in anemic patients: in anemic patients, the rate increased from 1.8% among those not transfused to 6.6% among those transfused three units (chi-square test for trend P < 0.0001), whereas in nonanemic patients, it increased from 1.7% among those not transfused to 3.2% among those transfused three units (chi-square test for trend P = 0.1). CONCLUSIONS: Anemic patients presenting for cardiac surgery are more susceptible to transfusion-related AKI than nonanemic patients. Interventions that reduce perioperative transfusions may protect anemic patients against AKI.
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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) | 0.000 | 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 it