Mortality outcomes in patients transfused with fresher versus older red blood cells: a meta‐analysis
Bibliographic record
Abstract
Background Among transfused patients, the effect of the duration of red blood cell storage on mortality remains unclear. This study aims to compare the mortality of patients who were transfused with fresher versus older red blood cells. Methods We performed an updated systematic search in the CENTRAL, MEDLINE, EMBASE and CINAHL databases, from January 2015 to October 2016. RCTs of hospitalized patients of any age comparing transfusion of fresher versus older red blood cells were eligible. We used a random‐effects model to calculate pooled risk ratios (RRs) with corresponding 95% confidence interval (CI). Results We identified 14 randomized trials that enrolled 26 374 participants. All‐cause mortality occurred in 1219 of 9531 (12·8%) patients who received a transfusion of fresher red blood cells and 1810 of 16 843 (10·7%) in those who received older red blood cells (RR: 1·04, 95% CI: 0·98–1·12, P = 0·90, I2 = 0%, high certainty for ruling out benefit of fresh blood, moderate certainty for ruling out harm of fresh blood). In six studies, in‐hospital death occurred in 691 of 7479 (9·2%) patients receiving fresher red cells and 1291 of 14 757 (8·8%) receiving older red cells (RR: 1·06, 95% CI: 0·97–1·15, P = 0·81, I2 = 0%, high certainty for ruling out benefit of fresh blood, moderate certainty for ruling out harm of fresh blood). Conclusion Transfusion of fresher red blood cells does not reduce overall or in‐hospital mortality when compared with older red blood cells. Our results support the practice of transfusing patients with the oldest red blood cells available in the blood bank.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.042 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".