The impact of blood transfusion on perioperative outcomes following gastric cancer resection: an analysis of the American College of Surgeons National Surgical Quality Improvement Program database
Bibliographic record
Abstract
BACKGROUND: Red blood cell transfusions (RBCT) carry risk of transfusion-related immunodulation that may impact postoperative recovery. This study examined the association between perioperative RBCT and short-term postoperative outcomes following gastrectomy for gastric cancer. METHODS: Using the American College of Surgeons National Surgical Quality Improvement Program database, we compared outcomes of patients (transfused v. nontransfused) undergoing elective gastrectomy for gastric cancer (2007-2012). Outcomes were 30-day major morbidity, mortality and length of stay. The association between perioperative RBCT and outcomes was estimated using modified Poisson, logistic, or negative binomial regression. RESULTS: Of the 3243 patients in the entire cohort, we included 2884 patients with nonmissing data, of whom 535 (18.6%) received RBCT. Overall 30-day major morbidity and mortality were 20% and 3.5%, respectively. After adjustment for baseline and clinical characteristics, RBCT was independently associated with increased 30-day mortality (relative risk [RR] 3.1, 95% confidence interval [CI] 1.9-5.0), major morbidity (RR 1.4, 95% CI 1.2-1.8), length of stay (RR 1.2, 95% CI 1.1-1.2), infections (RR 1.4, 95% CI 1.1-1.6), cardiac complications (RR 1.8, 95% CI 1.0-3.2) and respiratory failure (RR 2.3, 95% CI 1.6-3.3). CONCLUSION: Red blood cell transfusions are associated with worse postoperative short-term outcomes in patients with gastric cancer. Blood management strategies are needed to reduce the use of RBCT after gastrectomy for gastric cancer.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".