Declining use of red blood cell transfusions for gastrointestinal cancer surgery: A population-based analysis.
Bibliographic record
Abstract
802 Background: Perioperative anemia is common in gastrointestinal (GI) cancer surgery patients and is often treated with red blood cell transfusion (RBCT), which carries risks for inferior oncologic outcomes. Despite level-1 evidence for restrictive transfusion strategies, RBCT use is often not consistent with guidelines leading to a high rate of unnecessary transfusions. Understanding of RBCT use at the population-level is necessary to develop system-level efforts to minimize perioperative RBCT for cancer. We sought to evaluate the secular trends of transfusion in a large North American population. Methods: We conducted a population-based retrospective cohort study of patients undergoing GI cancer resection between 2007-2018 using linked administrative health datasets in Ontario, Canada. Primary outcome was administration of any RBCT during the hospitalization. Temporal RBCT trends were analyzed with Cochran-Armittage tests for trend. Modified Poisson regression assessed trends while controlling for potential confounders. Results: Of 79,764 patients undergoing GI cancer resection, median age was 69 (IQR: 60-78) years old and 55.5% were male. The most frequent cancer site was colorectal cancer (n = 63,243), followed by esophago-gastric (n = 7,307), hepato-pancreato-biliary (n = 6,510), and small bowel (n = 2,704). 30% of patients received RBCT. The proportion of patients transfused decreased from 26.5% in 2007 to 18.9% in 2018 (p < 0.001). This trend remained consistent when stratified by sex, age, cancer type, operative approach, procedure setting, and institution teaching status. After adjusting for patient and institution factors, the time period was associated with receipt of RBCT with a relative risk of 0.94 (95% CI 0.91-0.96) for 2011-14 and 0.75 (95% CI 0.73-0.78) for 2015-2018 compared to the period of 2007-10. Conclusions: Over the 11-year study period, we observed a decrease in RBCT for GI cancer resection. These findings may reflect the dissemination of clinical guidelines and implementation of patient blood management programs. An evaluation of institutional variation and the relationship with outcomes is warranted to identify opportunities for further improvement.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".