Survival after ultramassive transfusion: a review of 1360 cases
Bibliographic record
Abstract
BACKGROUND: Information about patient survival after transfusion of multiple blood volumes is limited, and most reports have focused on trauma patients. STUDY DESIGN AND METHODS: Retrospective study of blood use and survival at 11 hospitals in six nations between 2009 and 2013. Ultramassive transfusion (UMT) was defined as transfusion of 20 or more red blood cell (RBC) units over the course of any 2 consecutive calendar days. RESULTS: A total of 1975 patients received UMT and a representative sample of 1360 patients was studied in detail. Patients were grouped into seven diagnostic categories: solid organ transplantation (n = 411), cardiac or major vascular surgery (n = 317), general surgery (n = 228), trauma (n = 221), general medicine (n = 124), obstetrics (n = 23), and other (n = 36). During the 7 days after initiation of UMT, these patients used more than 120,000 blood components. The median (interquartile range) blood use was 35 (26-50) RBC units, 30 (20-47) plasma units, and 7 (4-13) platelet doses. Five- and 30-day survival significantly declined with increasing RBC use. Overall survivals of patients receiving UMT were 71% (5 day) and 60% (30 day), and in the subset of 165 patients receiving 60 or more RBC units over 2 consecutive days, 5-day survival was 54% ranging from 17% (trauma) to 75% (solid organ transplant). The decline in survival with increasing RBC transfusions was minimal for patients undergoing solid organ transplantation and was most pronounced for trauma and nonsurgical bleeding patients. CONCLUSION: Trauma was not the leading cause of UMT. Increasing RBC requirements were significantly associated with decreasing survival. However, survival was more strongly associated with diagnostic category than total RBCs transfused, with highest survival rates in solid organ transplant surgery.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".