The impact of perioperative red blood cell transfusion on long-term survival after hepatectomy for colorectal liver metastases.
Bibliographic record
Abstract
725 Background: Perioperative red blood cell transfusions (RBCT) are associated with postoperative morbidity and may increase cancer recurrence through immunologic mechanisms following resection of colorectal liver metastases (CRLM). We sought to explore the relationship between RBCTs and long-term survival following resection of CRLM in the contemporary surgical era. Methods: We conducted a retrospective review of a prospective database including all patients undergoing partial hepatectomy for CRLM from 2003-2012. Data regarding date of death was abstracted from a validated, population-based cancer registry. Primary outcome was overall survival (OS), compared based on RBCT (defined as time of surgery to 30 days following surgery) and on number of RBC units received using Kaplan-Meier curves. Cox regression analysis was performed to examine the association between RBCT and OS, while adjusting for prognostic factors including Fong score and period of treatment (2003-2007 vs. 2008-2012). Results: We included 483 patients operated for CRLM, of which 27.5% received RBCT. 90-day post-operative mortality was 4.8% and median follow-up was 33 (IQR: 20.1-54.8) months. Median survival in patients who received RBCT was 44.5 months compared with 93.5 months in patients who did not(p<0.0001). The difference persisted in subgroup analysis excluding patients who died within 90 days of surgery (62.3 vs. 93.5 months, p=0.023). After adjustment for Fong score and period of treatment, RBCT was independently associated with decreased OS (HR 2.15; 95% CI: 1.52-3.04). Conclusions: Perioperative RBCT is independently associated with decreased OS following hepatectomy for CRLM. Interventions to minimize and rationalize the use of RBCT for hepatectomy are warranted in order to mitigate this detrimental effect on long-term outcomes.
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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 | 0.000 |
| 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 | 0.000 |
| 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".