State of the Evidence for Prehospital Plasma Infusion for Patients With Suspected Traumatic Hemorrhage: A Rapid Review by the Prehospital Evidence-Based Practice Program
Bibliographic record
Abstract
Hemorrhage is the leading cause of preventable early trauma death. Replacement of volume and clotting factors is a critical component to reduce morbidity and mortality. The objective of this rapid systematic review was to evaluate the outcomes related to the administration of plasma in patients with traumatic hemorrhage in a prehospital environment. PubMed was searched for studies reporting plasma administration in prehospital patients with suspected traumatic hemorrhage. We reported each study's demographics, settings, and outcomes using the Prehospital Evidence-Based Practice Level and Direction of Evidence matrix. The search retrieved 1,008 titles, of which 43 articles were included. Most studies were randomized controlled trials or randomized controlled trial reanalyses, and the most common setting was critical care transport (CCT). Prehospital plasma was supported as the primary outcome of mortality in 73% of the studies. A total of 13 adverse events were reported across all studies, and plasma delivery was correlated with hypocalcemia in 1 study. High-quality evidence (Level and Direction of Evidence I) supports the delivery of plasma in the CCT prehospital setting to improvement of 30-day mortality for a range of patient presentations. The addition of plasma as part of a robust trauma resuscitation strategy in the CCT air medical setting is feasible and cost-effective.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.012 | 0.011 |
| 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) | 0.005 | 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".