Relationship of Erythrocyte Transfusion with Short- and Long-term Mortality in a Population-based Surgical Cohort
Bibliographic record
Abstract
BACKGROUND: When comparing transfused versus nontransfused patients, erythrocyte transfusion is consistently associated with increased mortality. Nonetheless, unmeasured confounding may unduly influence this comparison. This unmeasured risk may have less influence on comparisons of patients undergoing surgery at hospitals with differing transfusion rates. METHODS: Administrative databases were used to conduct a population-based cohort study of patients who underwent elective hip- or knee-replacement surgery from 1999 to 2008 in Ontario, Canada. The authors used Cox proportional-hazards models to determine the adjusted association of hospital-specific erythrocyte transfusion rates (i.e., comparing hospitals with differing transfusion rates) with postoperative mortality. For comparison, they also determined the adjusted association of patient receipt of transfusion (i.e., comparing transfused vs. nontransfused patients) with mortality. RESULTS: Of 162,190 patients, 23% (n=37,015) were transfused. Hospital-specific transfusion rates at the 66 included hospitals ranged from 10.3 to 57.9%. Compared with nontransfused patients, transfused patients experienced increased adjusted 30-day (hazard ratio 2.32; 95% CI, 1.91-2.83) and 1-yr mortality (hazard ratio 1.75; 95% CI, 1.60-1.91). However, when hospitals were categorized into quartiles based on hospital-specific transfusion rates, mortality rates were similar (highest transfusion quartile vs. lowest transfusion quartile: 30-day mortality, hazard ratio 1.11, 95% CI 0.82-1.50; 1-yr mortality, hazard ratio 1.02, 95% CI 0.82-1.26). CONCLUSIONS: The association of transfusion with postoperative mortality differed significantly when comparing transfused versus nontransfused patients, as opposed to comparing hospitals with differing transfusion rates. This discrepancy raises questions about the true relationship between transfusion and mortality.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".