The hyperfibrinolytic phenotype is the most lethal and resource intense presentation of fibrinolysis in massive transfusion patients
Bibliographic record
Abstract
BACKGROUND: Among bleeding patients, we hypothesized that the hyperfibrinolytic (HF) phenotype would be associated with the highest mortality, whereas shutdown (SD) patients would have the greatest complication burden. METHODS: Severely injured patients predicted to receive a massive transfusion at 12 Level I trauma centers were randomized to one of two transfusion ratios as described in the Pragmatic, Randomized, Optimal Platelet and Plasma Ratio trial. Fibrinolysis phenotypes were determined based on admission clot lysis at 30 minutes (LY30): SD ≤0.8%, physiologic (PHYS) 0.9-2.9%, and HF ≥3%. Univariate and multivariate analysis was performed. Logistic regression was used to adjust for age, gender, arrival physiology, shock, injury severity, center effect, and treatment arm. RESULTS: Among the 680 patients randomized, 547(80%) had admission thrombelastography (TEG) values available to determine fibrinolytic phenotypes. Compared to SD and PHYS, HF patients had higher Injury Severity Score (25 vs. 25 vs. 34), greater base deficit (-8 vs. -6 vs. -12) and were more uniformly hypocoagulable on admission by PT, PTT, and TEG values; all p <0.001. HF patients also received more red blood cells, plasma, and platelets (at 3, 6, and 24 hours); had fewer ICU-, ventilator-, and hospital-free days; and had higher 24-hour and 30-day mortality. There were no differences in complications between the three phenotypes. Multivariate logistic regression demonstrated that HF on admission was associated with a threefold higher mortality (OR 3.06, 95% CI 1.57-5.95, p = 0.001). CONCLUSIONS: Previous data have shown that both the SD and HF phenotypes are associated with increased mortality and complications in the general trauma population. However, in a large cohort of bleeding patients, HF was confirmed to be a much more lethal and resource-intense phenotype. These data suggest that further research into the understanding of SD and HF is warranted to improve outcomes in this patient population. LEVEL OF EVIDENCE: Prognostic, level II.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".