Impact of red blood cell transfusion strategies in haemato‐oncological patients: a systematic review and meta‐analysis
Bibliographic record
Abstract
Haemato-oncological patients receive many red blood cell (RBC) transfusions, however evidence-based guidelines are lacking. Our aim is to quantify the effect of restrictive and liberal RBC transfusion strategies on clinical outcomes and blood use in haemato-oncological patients. A literature search, last updated on 11 August 2016, was performed in PubMed, EMBASE (Excerpta Medica Database), Web of Science, Cochrane, CINAHL (Cumulative Index to Nursing and Allied Health Literature) and Academic Search Premier without restrictions on language and year of publication. Randomized controlled trials and observational studies that compared different RBC transfusion strategies in haemato-oncological patients were eligible for inclusion. Risk of bias assessment according to the Cochrane collaboration's tool and Newcastle-Ottawa scale was performed. After removing duplicates, 1142 publications were identified. Eventually, 15 studies were included, reporting on 2636 patients. The pooled relative risk for mortality was 0·68 [95% confidence interval (CI) 0·46-1·01] in favour of the restrictive strategy. The mean RBC use was reduced with 1·40 units (95% CI 0·70-2·09) per transfused patient per therapy cycle in the restrictive strategy group. There were no differences in safety outcomes. All currently available evidence suggests that restrictive strategies do not have a negative impact regarding clinical outcomes in haemato-oncological patients, while it reduces RBC use and associated costs.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.035 |
| 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".