The impact of red blood cell transfusion on mortality and treatment efficacy in patients treated with radiation: A systematic review
Bibliographic record
Abstract
INTRODUCTION: Packed red blood cell (RBC) transfusion is frequently used in patients undergoing radiotherapy (RT) because retrospective data suggest that anemic patients may respond sub-optimally to RT. No high-quality evidence currently exists to guide transfusion practices and establish hemoglobin (Hb) transfusion thresholds for this patient population, and practice varies significantly across centers. This systematic review investigated whether maintaining higher Hb via transfusion in radiation oncology patients leads to improved outcomes. METHODS: We performed a literature search of studies comparing RBC transfusion thresholds in radiation oncology patients. Included studies assessed patients receiving RT for malignancy of any diagnosis or stage. Excluded studies did not evaluate Hb or transfusion as an intervention or outcome. The primary outcome was overall survival. Secondary outcomes included locoregional control, number of transfusions and adverse events. RESULTS: One study met inclusion criteria. The study pooled results from two randomized controlled trials that stratified anemic patients with head and neck squamous cell carcinoma to RBC transfusion versus no transfusion. The study found no significant differences in overall survival or locoregional control after five years, despite increased Hb levels in the transfused group. We conducted a narrative review by extracting data from 10 non-comparative studies involving transfusion in patients receiving RT. Results demonstrated no consistent conclusions regarding whether transfusions improve or worsen outcomes. CONCLUSIONS: There is a lack of data on the effects of RBC transfusion on outcomes in patients undergoing RT. Well-designed prospective studies are needed in this area.
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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 | 0.005 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".