Liberal versus restrictive red blood cell transfusion in oncology practice: a systematic review
Bibliographic record
Abstract
Background The optimal strategy for red blood cell transfusion in the context of oncology continues to be a contentious issue. This systematic review aims to critically analyze the comparative safety and efficacy of liberal versus restrictive transfusion protocols within various subpopulations of oncology patients globally. Patients and methods A comprehensive search of the literature from 2014 to 2023 was conducted utilizing databases such as PubMed, Google Scholar, Cochrane Library, SciSpace, and Directory of Open Access Journals. Eligible studies comprised both randomized and nonrandomized clinical trials that examined transfusion thresholds among a variety of oncology patients undergoing chemotherapy, radiotherapy, surgical interventions, or palliative care. The assessment of risk of bias was performed employing the Cochrane Risk of Bias 2 and Newcastle-Ottawa scales. Results A total of six studies, encompassing 6,630 participants, fulfilled the inclusion criteria. Restrictive transfusion strategies (with hemoglobin thresholds of 7–8 g/dl) were associated with decreased transfusion rates and postoperative complications, without a concomitant increase in mortality or morbidity. Several studies indicated a possible trend toward enhanced outcomes with restrictive strategies, particularly in patients with hematologic malignancies. Conclusion Restrictive red blood cell transfusion strategies appear to be both safe and effective for stable oncology patients, potentially mitigating complications and conserving medical resources. The necessity for individualized transfusion decisions remains paramount.
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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 | 0.010 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".