Transfusion of fresher vs older red blood cells in hospitalized patients: a systematic review and meta-analysis
Bibliographic record
Abstract
The impact of transfusing fresher vs older red blood cells (RBCs) on patient-important outcomes remains controversial. Two recently published large trials have provided new evidence. We summarized results of randomized trials evaluating the impact of the age of transfused RBCs. We searched MEDLINE, EMBASE, CINAHL, the Cochrane Database for Systematic Reviews, and Cochrane CENTRAL for randomized controlled trials enrolling patients who were transfused fresher vs older RBCs and reported outcomes of death, adverse events, and infection. Independently and in duplicate, reviewers determined eligibility, risk of bias, and abstracted data. We conducted random effects meta-analyses and rated certainty (quality or confidence) of evidence using the GRADE approach. Of 12 trials that enrolled 5229 participants, 6 compared fresher RBCs with older RBCs and 6 compared fresher RBCs with current standard practice. There was little or no impact of fresher vs older RBCs on mortality (relative risk [RR], 1.04; 95% confidence interval [CI], 0.94-1.14; P = .45; I(2) = 0%, moderate certainty evidence) or on adverse events (RR, 1.02; 95% CI, 0.91-1.14; P = .74; I(2) = 0%, low certainty evidence). Fresher RBCs appeared to increase the risk of nosocomial infection (RR, 1.09; 95% CI, 1.00-1.18; P = .04; I(2) = 0%, risk difference 4.3%, low certainty evidence). Current evidence provides moderate certainty that use of fresher RBCs does not influence mortality, and low certainty that it does not influence adverse events but could possibly increase infection rates. The existing evidence provides no support for changing practices toward fresher RBC transfusion.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.036 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".