Monitoring unfractionated heparin therapy in dogs by measuring thrombin generation
Bibliographic record
Abstract
BACKGROUND: The calibrated automated thrombogram (CAT), an assay that permits measurement of thrombin generation in plasma, may be useful in studying hemostatic disorders and anticoagulant therapy in animals. OBJECTIVES: The aims of the study were to measure thrombin generation in healthy Beagle dogs and to evaluate the potential use of the CAT assay for monitoring therapy with unfractionated heparin (UFH). METHODS: Individual platelet-poor plasma samples and a plasma pool from 20 healthy adult Beagles were prepared. Serial UFH plasma dilutions were used to establish an in vitro heparin-sensitivity curve. The pharmacodynamic effects of heparin in vivo were evaluated in Beagles using the CAT assay to measure thrombin generation with tissue factor at a concentration of 5 pM for initiation. RESULTS: In healthy Beagles, the range of endogenous thrombin potential (ETP) was 238.7-414.0 nM/min (mean ± SD, 340.4 ± 63.1 nM/min). ETP intra-assay and interassay variations were 7.1% and 12.9%, respectively. In vitro, a UFH concentration ≥0.4 U/mL resulted in total inhibition of thrombin generation. In vivo, the maximal effect of UFH on ETP was observed at 170 ± 36 minutes (range, 120-210 minutes) and resulted in a decrease in ETP of 38.5 ± 7.8% (range, 26.5-50.3%). In 210-420 minutes, ETP returned to baseline in 5 dogs. CONCLUSION: Our study demonstrates that thrombin generation can be measured in canine plasma and may be useful in assessing the degree of anticoagulation provided by UFH.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 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".