Does the evidence support the importance of high transfusion ratios of plasma and platelets to red blood cells in improving outcomes in severely injured patients: a systematic review and meta‐analyses
Bibliographic record
Abstract
BACKGROUND: Deaths by exsanguination in trauma are preventable with hemorrhage control and resuscitation with allogeneic blood products (ABPs). The ideal transfusion ratio is unknown. We compared efficacy and safety of high transfusion ratios of FFP:RBC and PLT:RBC with low ratios in trauma. STUDY DESIGN AND METHODS: Medline, Embase, Cochrane, and Controlled Clinical Trials Register were searched. Observational and randomized data were included. Risk of bias was assessed using validated tools. Primary outcome was 24-h and 30-day mortality. Secondary outcomes were exposure to ABPs and improvement of coagulopathy. Meta-analysis was conducted using a random-effects model. Strength and evidence quality were graded using GRADE profile RESULTS: 55 studies were included (2 randomized and 53 observational), with low and moderate risk of bias, respectively, and overall low evidence quality. The two RCTs showed no mortality difference (odds ratio [OR], 1.35; 95% confidence interval [CI], 0.40-4.59). Observational studies reported lower mortality in high FFP:RBCs ratio (OR, 0.38 [95% CI, 0.22-0.68] for 1:1 vs. <1:1; OR, 0.42 [95% CI, 0.22-0.81] for 1:1.5 vs. <1:1.5; and OR, 0.47 [95% CI, 0.31-0.71] for 1:2 vs. <1:2, respectively). Meta-analyses in observational studies showed no difference in exposure to ABPs. No data on coagulopathy for meta-analysis was identified. CONCLUSIONS: Meta-analyses in observational studies suggest survival benefit and no difference in exposure to ABPs. No survival benefit in RCTs was identified. These conflicting results should be interpreted with caution. Studies are mostly observational, with relatively small sample sizes, nonrandom treatment allocation, and high potential for confounding. Further research is warranted.
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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 | 0.024 | 0.062 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.027 | 0.043 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".