The role of factor V in trauma-induced coagulopathy: an observational and experimental study
Bibliographic record
Abstract
Background In bleeding patients with trauma-induced coagulopathy (TIC), factor (F)V becomes depleted, which may not be corrected with existing treatment strategies. It is currently unknown whether supplementing FV or FVa improves TIC. Objectives To explore the relationship between FV activity and mortality in trauma patients, and to investigate the effect of FV(a) supplementation in addition to other treatment strategies in an in vitro model of TIC. Methods The association between FV activity and mortality was studied using an international prospective cohort study of trauma patients. In an in vitro whole blood and plasma model of TIC, the effect of FV(a) on rotational thromboelastometry and fibrin formation was studied. Effects of FV(a) were evaluated either as a standalone therapy or as adjunctive therapy in combination with tranexamic acid, fibrinogen concentrate, and/or prothrombin complex concentrate. Results A total of 1285 patients were included, with a median injury severity score of 16 (interquartile range: 8-26). Decreased FV activity was associated with increased mortality. In the whole blood TIC model, FVa increased maximum clot firmness and reduced fibrinolysis, both as a single and combination therapy. In the plasma TIC model, with lower tissue factor concentrations than in the whole blood model, FV(a) prolonged clotting times, both as a single treatment and in combination with other treatments. Conclusion FV depletion after trauma is associated with increased mortality. In an in vitro model of TIC, FV(a) results in procoagulant, antifibrinolytic, and anticoagulant effects. Further research is highly warranted.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".