The impact of perioperative transfusions on the oncologic outcomes of patients with ovarian cancer: A population‐based study
Bibliographic record
Abstract
Perioperative blood transfusion in ovarian cancer patients was associated with a 28% increase in all-cause mortality. The negative impact of perioperative blood transfusion extends beyond the immediate postoperative period. OBJECTIVES: The effect of perioperative blood transfusions on long-term oncologic outcomes of patients with advanced ovarian cancer undergoing cytoreductive surgery remains uncertain. Our study aims to determine the association between perioperative blood transfusion and all-cause mortality in this population. METHODS: Using province-wide administrative databases, patients with advanced ovarian cancer who underwent surgery between 2007 and 2021 as part of first-line treatment were identified. Perioperative transfusion was defined as any transfusion from date of surgery to discharge from hospital. Multivariable Cox proportional hazards regression models were used to determine if there was an independent association of transfusion with all-cause mortality, accounting significant confounders. RESULTS: A total of 5891 patients had cytoreductive surgery for advanced ovarian cancer between 2007 and 2021, of which 2898 (49.2%) had interval cytoreductive surgery (ICS) and 2993 (50.8%) had primary cytoreductive surgery (PCS). Perioperative blood transfusion was given to 37.3% of patients (40.5% ICS and 34.2% PCS). On multivariable analysis, there was an increased hazard of all-cause mortality for patients receiving perioperative transfusion compared to those who did not (hazard ratio: 1.28; 95% CI: 1.20-1.37). The association of increased all-cause mortality was observed starting 1 year after surgery, was sustained thereafter, and seen in both ICS and PCS groups. CONCLUSION: Perioperative blood transfusion after cytoreductive surgery for ovarian cancer is common in Ontario, Canada and was significantly associated with an increase in all-cause mortality. Blood transfusion is a poor prognostic factor, and the negative impact of blood transfusion persists beyond the immediate postoperative period.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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".