Early high ratio plasma to packed red blood cell massive transfusion in major trauma patients improves survival in a Canadian level I trauma center
Bibliographic record
Abstract
BackgroundHemorrhage continues to be the leading cause of death in major trauma. The concept of damage control resuscitation has gained popularity in both army and civilian setting, and early resuscitation with a high ratio plasma to packed red blood cell transfusion in a protocolized setting has been associated with improved survival, reduced complications and improved resource utilization.Research objectiveWe aim to determine if higher ratio plasma to packed red cell (FFP:PRBC) transfusion has an impact on survival in our Canadian level 1 trauma center.MethodsThis is a retrospective study incorporating all the trauma victims who received any blood transfusion in the period between July 2008 and December 2010. Of those, patients who received >6 units of packed red cells in the first 24 hours were identified using the trauma and blood bank registry. Patients were divided into high plasma to packed red cell (FFP:PRBC > 1:1.5) and low plasma to packed red cell ratio (FFP:PRBC < 1:1.5) groups. Basic demographic data and detailed transfusion data were collected, along with in hospital mortality, hospital and intensive care unit length of stay.ResultsThe high ratio FFP:PRBC group had a lower mortality rate (22%) compared to the low ratio group (34.4%), (P=0.22). Multivariate logistic regression model identified low ratio FFP:PRBC transfusion as an independent predictor of same admission mortality (odds 10.21, 95%CI 1.33-78.28, P=0.03). Linear regression model did not identify any significant independent predictor of hospital or intensive care unit length of stay.ConclusionLower ratio FFP:PRBC massive transfusion in major trauma independently predicts mortality, and high ratio protocolized resuscitation in major trauma is recommended.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".