The Initial Trauma Center Fluid Management of Penetrating Injury: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Damage-control resuscitation is the prevailing trauma resuscitation technique that emphasizes early and aggressive transfusion with balanced ratios of red blood cells (RBCs), plasma (FFP), and platelets (Plt) while minimizing crystalloid resuscitation, which is a departure from Advanced Trauma Life Support (ATLS) guidelines. It is unclear whether the newer approach is superior to the approach recommended by ATLS. QUESTIONS/PURPOSES: With these recent changes pervading resuscitation protocols, we performed a systematic review to determine if the shift in trauma resuscitation from ATLS guidelines to damage control resuscitation has improved mortality in patients with penetrating injuries. METHODS: A systematic search of PubMed, the Cochrane Library, and the Current Controlled Trials Register was performed for studies comparing mortality in massively transfused penetrating trauma patients receiving either balanced ratios of blood transfusion per damage control resuscitation tenets or undergoing an alternate blood volume resuscitation strategy. Studies were deemed appropriate for inclusion if they had a Newcastle-Ottawa Scale score of 6 or greater as well as at least 30% penetrating trauma. Twenty studies that reported on a total of 12,154 patients were included. RESULTS: Transfusion ratios varied widely, with 1:1 and 1:2 ratios of FFP:RBC most often defined as high ratios for purposes of comparison with other low ratio groups. Fourteen of 20 studies found significantly lower 30-day mortality when higher transfusion ratios of FFP, RBC, and/or Plt were used; six of 20 studies found mortality to be similar between higher and lower transfusion ratios. CONCLUSIONS: Patients with penetrating injuries who require massive transfusion should be transfused early using balanced ratios of RBC, FFP, and Plt. Randomized, controlled trials are needed to determine optimal ratios for transfusion.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".