The impact of perioperative blood transfusions on short-term outcomes following hepatectomy
Bibliographic record
Abstract
Background: Bleeding and need for red blood cell transfusions (RBCT) remain a significant concern with hepatectomy. RBCT carry risk of transfusion-related immunomodulation that may impact post-operative recovery. This study soughs to assess the association between RBCT and post-hepatectomy morbidity. Methods: Using the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) registry, we identified all adult patients undergoing elective hepatectomy over 2007–2012. Two exposure groups were created based on RBCT. Primary outcomes were 30-day major morbidity and mortality. Secondary outcomes included 30-day system-specific morbidity and length of stay (LOS). Relative risks (RR) with 95% confidence interval (95% CI) were computed using regression analyses. Sensitivity analyses were conducted to understand how missing data might have impacted the results. Results: A total of 12,180 patients were identified. Of those, 11,712 met inclusion criteria, 2,951 (25.2%) of whom received RBCT. Major morbidity occurred in 14.9% of patients and was strongly associated with RBCT (25.3% vs. 11.3%; P<0.001). Transfused patients had higher rates of 30-day mortality (5.6% vs. 1.0%; P<0.0001). After adjustment for baseline and clinical characteristics, RBCT was independently associated with increased major morbidity (RR 1.80; 95% CI: 1.61–1.99), mortality (RR 3.62; 95% CI: 2.68–4.89), and 1.29 times greater LOS (RR 1.29; 95% CI: 1.25–1.32). Results were robust to a number of sensitivity analyses for missing data. Conclusions: Perioperative RBCT for hepatectomy was independently associated with worse short-term outcomes and prolonged LOS. These findings further the rationale to focus on minimizing RBCT for hepatectomy, when they can be avoided.
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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 | 0.033 |
| 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 | 0.001 |
| Open science | 0.000 | 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".