Coagulopathy of massive transfusion: pathophysiology and monitoring
Bibliographic record
Abstract
Coagulopathy associated with massive transfusion (MT) remains an important clinical problem. The author attempted to identify the causes of coagulopathy in massively transfused, adult and previously haemostatically competent patients and to differentiate between the elective surgical and the emergency settings. A MEDLINE search was conducted for articles published on ‘massive transfusion’, ‘transfusion’, ‘trauma’, ‘surgery’, ‘coagulopathy’ and ‘haemostatic defects’. A narrative format was adopted. Coagulopathy associated with MT is an intricate, multifactorial and multicellular event. In patients undergoing elective surgery, a decrease in fibrinogen concentration is observed initially while thrombocytopenia is a late occurrence. Critically low levels of coagulation factors were seldom reported when whole blood was in common use. With the use of packed red blood cells (PRBC), dilution or consumption of coagulation factors has become a significant issue requiring specific treatment with, primarily, fresh frozen plasma (FFP). In the emergency setting (e.g., trauma, ruptured abdominal aortic aneurysm), tissue trauma, shock, tissue anoxia and hypothermia contribute to the development of disseminated intravascular coagulation and microvascular bleeding. It has been shown that the proactive administration of platelets and FFP improves coagulation, decreases haemorrhage and improves survival in these massively bleeding patients. We can only speculate that in this specific context, the benefits of early and aggressive platelet and coagulation factor replacement are related to the ongoing consumption coagulopathy at the time of 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 | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".