Evaluation of the Antithrombotic Effects of Rivaroxaban and Apixaban Using the Total Thrombus-Formation Analysis System<sup>®</sup>: <i>In Vitro</i> and <i>Ex Vivo</i> Studies
Bibliographic record
Abstract
Background: The usefulness of the Total Thrombus-Formation Analysis System ® (T-TAS ® ) for monitoring the anticoagulant effects of non-vitamin K oral anticoagulants (NOACs) in clinical practice has been poorly addressed. Methods: NOACs (rivaroxaban and apixaban) were added to whole blood from healthy subjects in an in vitro study, and their effects on thrombus formation were evaluated by the T-TAS ® . We also evaluated antithrombotic effects using ex vivo samples of whole blood from patients given rivaroxaban or apixaban at the respective trough and peak drug concentrations. Results: T-TAS ® could determine anticoagulant effects in whole blood treated with rivaroxaban or apixaban in vitro . The increases in the anticoagulant effects of rivaroxaban and apixaban from the trough to peak concentrations in whole blood were successfully monitored by the T-TAS ® using ex vivo samples. The antithrombotic effects of rivaroxaban and apixaban (in terms of factor Xa inhibition) at the peak were strongly linked to those at the trough. Conclusion: T-TAS ® could be a clinically useful tool for monitoring the anticoagulant effects of factor Xa inhibitors, and may represent an accurate quantitative analysis. J Clin Med Res. 2016;8(12):899-907 doi: http://dx.doi.org/10.14740/jocmr2773w
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".