Plasma transfusion for patients with severe hemorrhage: what is the evidence?
Bibliographic record
Abstract
The following review will detail the current knowledge in massive hemorrhage with regard to the pathophysiology of the coagulation disturbance, the role of plasma, the role of alternatives to plasma, and the clinical value of having a massive transfusion protocol. The coagulation disturbance in trauma patients is more than just the result of consumption of clotting factors at sites of injury and dilution from the infusion of intravenous fluids and red blood cells (RBCs). Even before substantial amounts of fluid resuscitation and RBC transfusion, one-quarter of trauma patients already have abnormal coagulation variables. There is an apparent role for the activation of protein C, hypofibrinogenemia, and fibrin(gen)olysis in the coagulation disturbance after trauma and massive hemorrhage. None of these three disturbances would be completely mitigated by the use of plasma alone, suggesting that there may be an opportunity to improve care of these patients with alternative strategies, such as fibrinogen concentrates and antifibrinolytics. Despite numerous retrospective cohort studies evaluating 1:1 plasma to RBC formula-driven resuscitation, the overall clinical value of this approach is unclear. Studies have even raised concerns regarding a potential increase in morbidity associated with this approach, particularly for patients overtriaged to 1:1 where a massive transfusion is unlikely. We also do not have sufficient evidence to recommend either goal-directed therapy with thromboelastography or early use of fibrinogen replacement, with either cryoprecipitate or fibrinogen concentrates. We have high-quality data that argue against the role for recombinant Factor VIIa that should prompt removal of this strategy from existing protocols. In contrast, we have high-level evidence that all bleeding trauma patients should receive tranexamic acid as soon as possible after injury. This therapy must be included in hemorrhage protocols. If we are to improve the care of massively bleeding patients on a firm scientific ground, we will need large-scale randomized trials to delineate the role of coagulation replacement and the utility of laboratory monitoring. But even until these trials are completed, it is clear that a massive transfusion protocol is needed in all hospitals that manage bleeding patients, to ensure a prompt and coordinated response to hemorrhage.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".