Uncrossmatched blood transfusions for trauma patients in the emergency department: incidence, outcomes and recommendations
Bibliographic record
Abstract
BACKGROUND: Early transfusion of blood products for severely injured patients can improve volume depletion, acidosis, dilution and coagulopathy. There is concern that some patients are unnecessarily exposed to the risks of emergent transfusion with uncrossmatched red blood cell products (URBC) in the emergency department (ED). The goal of this study was to evaluate the transfusion practices in our ED among all patients who received URBC. METHODS: We analyzed all injured patients transfused at least 1 URBC in the ED at a level-1 trauma centre between Jan. 15, 2007, and Jan. 14, 2008. Demographics, injuries and outcomes were reported. We used standard statistical methodology. RESULTS: At least 1 URBC product was transfused into 153 patients (5% of all patients, mean 2.6 products) in the ED (median Injury Severity Score [ISS] 28; hemodynamic instability 94%). Sixty-four percent of patients proceeded to an emergent operation and 17% required massive transfusion. The overall mortality rate was 45%, which increased to 52% and 100% in patients who received 4 and 5 or more URBC products, respectively. Nonsurvivors had a higher median ISS (p=0.017), received more URBC in the ED (p=0.006) and possessed more major vascular injuries (p<0.001). Among nonsurvivors, 67% died of uncontrollable hemorrhage. Unnecessary URBC transfusions in the ED occurred in 7% of patients. CONCLUSION: Overtransfusion was minimal based on clinical acumen triggers. Early transfer of patients receiving URBC products in the ED to the operating room, intensive care unit or angiography suite for ongoing resuscitation and definitive hemorrhage control must be strongly considered.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".