The fibrinogen but not the <scp>F</scp>actor <scp>VIII</scp> content of transfused plasma determines its effectiveness at reducing bleeding in coagulopathic mice
Bibliographic record
Abstract
BACKGROUND: The evidence supporting plasma transfusion as a means to restore hemostatic control and prevent or treat bleeding is weak, leading to uncertainties as to which proteins affect the therapeutic quality of plasma. Some regulators focus on coagulation Factor (F)VIII activity, but whether this measure reflects overall transfusable plasma efficacy is questionable. We developed a mouse model of coagulopathy in which bleeding outcomes were responsive to plasma transfusion and addressed the relative contributions of FVIII and fibrinogen (Fg) to plasma quality. STUDY DESIGN AND METHODS: Anesthetized mice were rendered coagulopathic by four rounds of exchange of whole blood for washed red blood cells (RBCs) in 5% human albumin solution (HAS), which reduced RBCs, platelets, and plasma protein levels by 55, 66, and 80% of starting levels, in a blood exchange-induced coagulopathy approach (BECA). Before tail vein transection, BECA mice were transfused with HAS, wild-type murine fresh-frozen plasma (WT mFFP), or mFFP from FVIII-/- or Fg-/- knockout mice. BECA mice were also subjected to laser-induced arteriolar injury and thrombus formation quantified by intravital microscopy. RESULTS: Transfusion of WT or FVIII-/- mFFP reduced blood loss by fourfold in BECA mice relative to HAS; Fg-/- mFFP had no effect. WT or FVIII-/- mFFP transfusion, but not that of Fg-/- mFFP, increased thrombus size in laser-injured BECA mice arterioles. Extended refrigerated storage of mFFP did not reduce its antihemorrhagic effects. CONCLUSIONS: The content of Fg, but not FVIII, determined the efficacy of plasma transfusion in coagulopathic mice.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".