A comparative study of tissue factor and kaolin on blood coagulation assays using rotational thromboelastometry and thromboelastography
Bibliographic record
Abstract
Rotational thromboelastometry (ROTEM) and thromboelastography (TEG) have been increasingly used to diagnose acute coagulopathy and guide blood transfusion. The tests are routinely performed using different triggering activators such as tissue factor and kaolin, which activate different pathways yielding different results. To optimize the global blood coagulation assays using ROTEM and TEG, we conducted a comparative study on the activation methods employing tissue factor and kaolin at different concentrations as well as standard reagents as recommended by the manufacturer of each device. Key parameter values were obtained at various assay conditions to evaluate and compare coagulation and fibrinolysis profiles of citrated whole blood collected from healthy volunteers. It was found that tissue factor reduced ROTEM clotting time and TEG R, and increased ROTEM clot formation time and TEG K in a concentration-dependent manner. In addition, tissue factor affected ROTEM alpha angle, and maximum clot firmness, especially in the absence of kaolin activation, whereas both ROTEM and TEG clot lysis (LI30, CL30, and LY30) remained unaffected. Moreover, kaolin reduced ROTEM clotting time and TEG R and K, but to a lesser extent than tissue factor, in-tem and ex-tem. Correlations in all corresponding parameters between ROTEM and TEG were observed, when the same activators were used in the assays compared with lesser correlations between standard kaolin TEG and ROTEM (INTEM/EXTEM). The two types of viscoelastic point-of-care devices provide different results, depending on the triggering reagent used to perform the assay. Optimal assay condition was obtained to reduce assay time and improve assay accuracy.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".