Monitoring of Rivaroxaban Therapy in Hypercoagulable Dogs
Bibliographic record
Abstract
Abstract Background Measurement of rivaroxaban efficacy using the rivaroxaban-specific anti-Xa assay (raXa) can be used for monitoring in veterinary medicine. Detection of rivaroxaban efficacy using other hemostatic tests would make monitoring timelier and more accessible. Objectives Compare results of raXa with prothrombin time (PT), activated partial thromboplastin time (aPTT), fibrinogen concentration, tissue factor (TF) and kaolin-activated thromboelastography (TEG), and thrombin generation (TG) in hypercoagulable dogs. Animals Twelve client-owned dogs, diagnosed with hypercoagulability or thromboembolic disease, and prescribed rivaroxaban, were recruited from a tertiary referral hospital from 2020 to 2022. Methods Prospective clinical trial. Jugular vein blood samples were collected before treatment, and 1 week and 1–3 months after initiation of rivaroxaban therapy. Hemostatic tests were performed at each visit (3 h after rivaroxaban dosing). TG curve parameters lag time, endogenous thrombin potential (ETP), peak, and time to peak (ttpeak) were assessed. Results There was a significant linear relationship between raXa and PT (r2 = 0.74, p < 0.001), ETP (r2 = 0.83, p < 0.001), lag time (r2 = 0.87, p < 0.001), peak (r2 = 0.86, p < 0.001), and ttpeak (r2 = 0.86, p < 0.001). There was a weak linear relationship between raXa and kaolin-activated TEG parameter reaction time (R) (r2 = 0.49, p = 0.026). There was no significant relationship between raXa and aPTT, fibrinogen concentration and the remainder of the TEG variables (p > 0.05). Conclusion and Clinical Importance PT and TG correlated with raXa. PT performed at a reference laboratory appeared to be a convenient method to monitor a small cohort of dogs receiving rivaroxaban therapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 teacher head, 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".