In Trauma, Conventional ROTEM and TEG Results Are Not Interchangeable But Are Similar in Clinical Applicability
Bibliographic record
Abstract
BACKGROUND: There is growing interest in viscoelastic hemostatic assays rotational thromboelastometry (ROTEM) and thromboelastography (TEG) for trauma. Despite shared features, it is unknown whether their results are interchangeable and whether one is clinically superior in predicting mortality, blood transfusion, and diagnosing early trauma coagulopathy. METHODS: We conducted a prospective observational study comparing equivalent ROTEM and TEG parameters. Severely injured patients expected to receive massive transfusion were included. Assays were performed simultaneously on admission and repeated over subsequent 12 hours. International normalized ratio ≥1.2 or fibrinogen <1 g/L defined coagulopathy. TEG used kaolin as coagulation initiator and ROTEM used tissue factor (conventional). Spearman nonparametric analysis and Bland-Altman difference mean plot revealed parameter association. Logistic regression and receiver operating characteristic curves measured predictive values. RESULTS: 33 patients (74 ROTEM, 74 TEG) were included; 79% were male, mean Injury Severity Score was 23.5 ± 14, admission international normalized ratio was 1.33 ± 0.4, and 63.4% received blood transfusions. Overall, parameter agreement fell outside acceptable limits, with weak or no association. Clinically, ROTEM maximum clot firmness and TEG maximum amplitude showed reasonable predictive accuracy for mortality, strong accuracy for any or massive blood transfusion, reasonable for plasma transfusion and similar poor predictive accuracy for diagnosing coagulopathy. CONCLUSIONS: ROTEM and TEG results are not interchangeable, arguably due to different coagulation triggers. Assays had similar clinical performance.
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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 | 0.018 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| 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".