The Role of TEG and ROTEM in Damage Control Resuscitation
Bibliographic record
Abstract
ABSTRACT: Trauma-induced coagulopathy is associated with very high mortality, and hemorrhage remains the leading preventable cause of death after injury. Directed methods to combat coagulopathy and attain hemostasis are needed. The available literature regarding viscoelastic testing, including thrombelastography (TEG) and rotational thromboelastometry (ROTEM), was reviewed to provide clinically relevant guidance for emergency resuscitation. These tests predict massive transfusion and developing coagulopathy earlier than conventional coagulation testing, within 15 min using rapid testing. They can guide resuscitation after trauma, as well. TEG and ROTEM direct early transfusion of fresh frozen plasma when clinical gestalt has not activated a massive transfusion protocol. Reaction time and clotting time via these tests can also detect clinically significant levels of direct oral anticoagulants. Slowed clot kinetics suggest the need for transfusion of fibrinogen via concentrates or cryoprecipitate. Lowered clot strength can be corrected with platelets and fibrinogen. Finally, viscoelastic tests identify fibrinolysis, a finding associated with significantly increased mortality yet one that no conventional coagulation test can reliably detect. Using these parameters, guided resuscitation begins within minutes of a patient's arrival. A growing body of evidence suggests this approach may improve survival while reducing volumes of blood products transfused.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".