Clinical review: Fresh frozen plasma in massive bleedings - more questions than answers
Bibliographic record
Abstract
Fresh frozen plasma (FFP) is indicated for the management of massive bleedings. Recent audits suggest physician knowledge of FFP is inadequate and half of the FFP transfused in critical care is inappropriate. Trauma is among the largest consumers of FFP. Current trauma resuscitation guidelines recommend FFP to correct coagulopathy only after diagnosed by laboratory tests, often when overt dilutional coagulopathy already exists. The evidence supporting these guidelines is limited and bleeding remains a major cause of trauma-related death. Recent studies demonstrated that coagulopathy occurs early in trauma. A novel early formula-driven haemostatic resuscitation proposes addressing coagulopathy early in massive bleedings with FFP at a near 1:1 ratio with red blood cells. Recent retrospective reports suggest such strategy significantly reduces mortality, and its use is gradually expanding to nontraumatic bleedings in critical care. The supporting studies, however, have bias limiting the interpretation of the results. Furthermore, logistical considerations including need for immediately available universal donor AB plasma, short life after thawing, potential waste and transfusion-associated complications have challenged its implementation. The present review focuses on FFP transfusion in massive bleeding and critically appraises the evidence on formula-driven resuscitation, providing resources to allow clinicians to develop informed opinion, given the current deficient and conflicting evidence.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".