Monitoring of Rivaroxaban Therapy in Hypercoagulable Dogs
Bibliographic record
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: = 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 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) | 0.000 | 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".