Sex‐mismatched red blood cell transfusions and mortality: A systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Selection of a compatible red blood cell (RBC) unit does not include matching for donor sex. This systematic review and meta-analysis aims to summarize the evidence examining the impact of sex-mismatched RBC transfusion on recipient mortality. MATERIALS AND METHODS: Ovid MEDLINE, Ovid EMBASE, CINAHL, PubMed, Web of Science and the Cochrane Database of Systematic Reviews were searched from inception up to 23 November 2018. Randomized controlled trials and observational studies were included in the search. Eligible studies reported on the impact of sex-matched compared to sex-mismatched RBC transfusion on recipient mortality. Two investigators independently extracted data and assessed study quality. A three-level meta-analytic model was applied to emphasize the unknown dependence among the effect sizes. RESULTS: Five retrospective observational studies (n = 86 737) were included; no RCTs were found. Sex-mismatched RBC transfusions were associated with a higher risk of death compared with sex-matched transfusions (pooled hazard ratio [HR]: 1·13; 95% confidence interval [CI]: 1·02-1·24). In the subgroup of cardiovascular surgery (n = 57 712), there was no significant increase in mortality with sex-mismatched transfusions (pooled HR: 1·08; 95% CI: 0·95-1·22). The data were prone to confounding, selection bias and reporting bias. Certainty of the evidence was very low. CONCLUSION: Sex-mismatched RBC transfusions were associated with an increased risk of death in this pooled analysis. However, the certainty of the evidence was very low from observational studies. The need to match donor and recipient sex for transfusions requires further investigation because of the potential widespread impact.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.031 |
| Bibliometrics | 0.006 | 0.006 |
| 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".