Systematic Review and Meta-Analysis on the Role of Perioperative Blood Transfusion in Patients Undergoing Radical Cystectomy for Urothelial Carcinoma
Bibliographic record
Abstract
BACKGROUND: Radical cystectomy (RC) is the standard of care in patients with muscle-invasive bladder cancer. The impact of perioperative red blood cell (RBC) transfusion on oncological outcomes after RC is not clearly established as the existing publications show conflicting results. OBJECTIVES: The aim of this systematic review and meta-analysis was to investigate the prognostic role of perioperative RBC transfusion on oncological outcomes after RC. METHODS: Systematic online search on PubMed was conducted, based on PRISMA criteria for publications reporting on RBC transfusion during RC. Publications with the following criteria were included: (I) reported data on perioperative blood transfusion; (II) Reported Hazard ratio (HR) and 95% -confidence interval (CI) for the impact of transfusion on survival outcomes. Primary outcome was the impact of perioperative RBC transfusion on recurrence-free survival (RFS), cancer-specific survival (CSS) and overall survival (OS). Risk of bias assessment was performed using Newcastle-Ottawa Scale. Statistical analysis was performed using Revman 5.4 software. RESULTS: From 27 primarily identified publications, 19 eligible articles including 22897 patients were selected. Perioperative RBC transfusion showed no impact on RFS ( Z = 1.34; p = 0,18) and significant negative impact on CSS ( Z = 2.67; p = 0.008) and OS ( Z = 3.22; p = 0.001). Intraoperative RBC transfusion showed no impact on RFS ( Z = 0.58; p = 0.56) and CSS ( Z = 1.06; p = 0.29) and OS ( Z = 1.47; p = 0.14). Postoperative RBC transfusion showed non-significant trend towards improved RFS ( Z = 1.89; p = 0.06) and no impact on CSS ( Z = 1.56; p = 0.12) and OS ( Z = 0.53 p = 0.60). CONCLUSION: In this meta-analysis, we found perioperative blood transfusion to be a significant predictor only for worse CSS and OS but not for RFS. This effect may be determined by differences in tumor stages and patient comorbidities for which this meta-analysis cannot control due to lack of respective raw data.
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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 | 0.014 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.034 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".