Bibliographic record
Abstract
SUMMARY Coagulopathy associated with massive transfusion remains an important clinical problem. In this paper, the objectives of the authors are two‐fold: to identify the causes of coagulopathy in massively transfused adult and previously hemostatically competent patients and to differentiate between the elective surgical setting and trauma, in order to recommend the most appropriate treatment strategies. A MEDLINE search was conducted for published articles on massive transfusion, using the terms “transfusion,”“trauma,”“surgery,”“coagulopathy,” and “hemostatic defects.” Articles were organized and reviewed by date of publication in order to better understand the evolution of our thinking on massive transfusion and coagulopathy. Coagulopathy associated with massive transfusion is an intricate, multifactorial, and multicellular event. In trauma patients, tissue trauma and anoxia, shock, and hypothermia contribute to the development of disseminated intravascular coagulation and microvascular bleeding. 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 cells, however, dilution or consumption of coagulation factors has become a significant issue requiring specific treatment with, primarily, fresh frozen plasma. Maintaining a normal body temperature is a simple and effective strategy to improve hemostasis during massive transfusion. Red cells play an important role and hematocrit levels as high as 35 to 36% may be required to sustain the process. The administration of platelets and/or fresh frozen plasma should be based on clinical judgment and coagulation testing results, and their use restricted to the treatment of clinical coagulopathies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".