The impact of a massive transfusion protocol (1:1:1) on major hepatic injuries: Does it increase abdominal wall closure rates?
Bibliographic record
Abstract
BACKGROUND: Massive transfusion protocols (MTPs) using high plasma and platelet ratios for exsanguinating trauma patients are increasingly popular. Major liver injuries often require massive resuscitations and immediate hemorrhage control. Current published literature describes outcomes among patients with mixed patterns of injury. We sought to identify the effects of an MTP on patients with major liver trauma. METHODS: Patients with grade 3, 4 or 5 liver injuries who required a massive blood component transfusion were analyzed. We compared patients with high plasma:red blood cell:platelet ratio (1:1:1) transfusions (2007-2009) with patients injured before the creation of an institutional MTP (2005-2007). RESULTS: Among 60 patients with major hepatic injuries, 35 (58%) underwent resuscitation after the implementation of an MTP. Patient and injury characteristics were similar between cohorts. Implementation of the MTP significantly improved plasma: red blood cell:platelet ratios and decreased crystalloid fluid resuscitation (p = 0.026). Rapid improvement in early acidosis and coagulopathy was superior with an MTP (p = 0.009). More patients in the MTP group also underwent primary abdominal fascial closure during their hospital stay (p = 0.021). This was most evident with grade 4 injuries (89% vs. 14%). The mean time to fascial closure was 4.2 days. The overall survival rate for all major liver injuries was not affected by an MTP (p = 0.61). CONCLUSION: The implementation of a formal MTP using high plasma and platelet ratios resulted in a substantial increase in abdominal wall approximation. This occurred concurrently to a decrease in the delivered volume of crystalloid fluid.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".