Fibrinogen Supplementation and Its Indications
Bibliographic record
Abstract
Adequate plasma levels of fibrinogen are essential for clot formation, and in severe bleeding, fibrinogen reaches a critically low plasma concentration earlier than other coagulation factors. Although the critical minimum concentration of fibrinogen to maintain hemostasis is a matter of debate, many patients with coagulopathic bleeding require fibrinogen supplementation. Among the treatment options for fibrinogen supplementation, fibrinogen concentrate may be viewed by some as preferable to fresh frozen plasma or cryoprecipitate. The authors review major studies that have assessed fibrinogen treatment in trauma, cardiac surgery, end-stage liver disease, postpartum hemorrhage, and pediatric patients. Some but not all randomized controlled trials have shown that fibrinogen concentrate can be beneficial in these settings. The use of fibrinogen as part of coagulation factor concentrate based therapy guided by point-of-care viscoelastic coagulation monitoring (ROTEM [rotational thromboelastometry] or TEG [thromboelastography]) appears promising. In addition to reducing patients' exposure to allogeneic blood products, this strategy may reduce the risk of complications such as transfusion-associated circulatory overload, transfusion-related acute lung injury, and thromboembolic adverse events. Randomized controlled trials are challenging to perform in patients with critical bleeding, and more evidence is needed in this setting. However, current scientific rationale and clinical data support fibrinogen repletion in patients with ongoing bleeding and confirmed fibrinogen deficiency.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".