Association between peri‐operative red blood cell transfusion and cancer recurrence in patients undergoing major cancer surgery: an umbrella review*
Bibliographic record
Abstract
INTRODUCTION: Peri-operative allogeneic red blood cell transfusion is hypothesised to increase the risk of cancer recurrence following cancer surgery. However, previous data supporting this association are limited by residual confounding. We conducted an umbrella review (i.e. a systematic review of systematic reviews) to synthesise and evaluate the evidence between red blood cell transfusion and cancer recurrence. METHODS: We searched online databases for systematic reviews of red blood cell transfusion and cancer-related outcomes. The AMSTAR 2 tool was used for quality assessment. The adequacy of confounding adjustment was judged according to a consensus-derived framework. RESULTS: We included five relevant systematic views which included patient populations ranging from 2110 to 184,190. Two reviews reported cancer recurrence, and all reported an association with red blood cell transfusion. Three reviews reported positive associations between red blood cell transfusion and adverse outcomes including all-cause mortality, recurrence-free survival and cancer-related mortality. According to AMSTAR 2, four reviews were rated as 'critically low quality' and one as 'low quality'. There was variation in how systematic reviews assessed the risk of bias from confounding. Compared with our pre-derived framework, we found a high likelihood of unmeasured confounding. DISCUSSION: Currently available evidence describes an association between peri-operative red blood cell transfusion and cancer recurrence, but this is mostly of low to critically low quality, with minimal control for residual confounding. Further research, at low risk of bias, is required to provide definitive evidence and inform practice.
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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 | 0.022 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".