Allogeneic Red Blood Cell Transfusion Is an Independent Risk Factor for the Development of Postoperative Bacterial Infection
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Allogeneic red blood cell transfusions may exert immunomodulatory effects in recipients including an increased rate of postoperative bacterial infection. It is controversial whether allogeneic transfusion is an independent predictor for the development of postoperative bacterial infection. METHODS: We analysed a prospectively collected database of 1,349 patients undergoing colorectal surgery in 11 centres across Canada. The primary outcome was the development of either a postoperative wound infection or intra-abdominal sepsis in transfused and nontransfused patients. The effect of allogeneic transfusion on postoperative infection was evaluated with adjustment for all the confounding factors in a multiple regression analysis. RESULTS: The 282 patients who received a total of 832 allogeneic units had a significantly higher frequency of wound infections and intra-abdominal sepsis than the patients who were not transfused (25. 9 vs. 14.2%, p = 0.001). A significant dose-response relationship between transfusion and infection rate was demonstrated. Multiple regression analysis identified allogeneic transfusion as a statistically significant independent predictor for postoperative bacterial infection (OR 1.18, 95% CI 1.05-1.33, p = 0.007). Other independent predictors were anastomotic leak, repeat operation, patient age and preoperative haemoglobin level. The mortality rate was also significantly higher in the transfused group. CONCLUSION: These data support the hypothesis that allogeneic red cell transfusion is an independent risk factor for the development of postoperative bacterial infection in patients undergoing colorectal surgery. This association provides further reason to minimise exposure to allogeneic transfusions in the perioperative setting.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".