Thromboelastography and Thromboelastometry in Assessment of Fibrinogen Deficiency and Prediction for Transfusion Requirement: A Descriptive Review
Bibliographic record
Abstract
Fibrinogen is crucial for the formation of blood clot and clinical outcomes in major bleeding. Both Thromboelastography (TEG) and Rotational Thromboelastometry (ROTEM) have been increasingly used to diagnose fibrinogen deficiency and guide fibrinogen transfusion in trauma and surgical bleeding patients. We conducted a comprehensive and comparative review on the technologies and clinical applications of two typical functional fibrinogen assays using TEG (FF TEG) and ROTEM (FIBTEM) for assessment of fibrinogen level and deficiency, and prediction of transfusion requirement. Clot strength and firmness of FF TEG and ROTEM FIBTEM were the most used parameters, and their associations with fibrinogen levels as measured by Clauss method ranged from 0 to 0.9 for FF TEG and 0.27 to 0.94 for FIBTEM. A comparison of the interchangeability and clinical performance of the functional fibrinogen assays using the two systems showed that the results were correlated, but are not interchangeable between the two systems. It appears that ROTEM FIBTEM showed better associations with the Clauss method and more clinical use for monitoring fibrinogen deficiency and predicting transfusion requirements including fibrinogen replacement than FF TEG. TEG and ROTEM functional fibrinogen tests play important roles in the diagnosis of fibrinogen-related coagulopathy and guidance of transfusion requirements. Despite the fact that high-quality evidence is still needed, the two systems are likely to remain popular for the hemostatic management of bleeding patients.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".