In vitro effects of dalteparin on thrombin generation in canine plasma
Bibliographic record
Abstract
Background The calibrated automated thrombogram (CAT) is a functional thrombin generation (TG) assay that may provide a new approach for monitoring anticoagulant therapy in dogs. The effects of dalteparin on TG variables in dogs are unknown. Objectives Objectives were to establish normal TG variable ranges in dogs and measure the in vitro TG variables in canine pooled platelet‐poor plasma (PPP) spiked with different dalteparin concentrations. Methods In the first experiment, plasma samples from 25 adult healthy Beagle dogs and 11 client‐owned healthy dogs of multiple breeds was measured individually for obtaining normal TG values. In the second experiment, separate pools of the remaining PPP from 24 of the 25 previous adult Beagles and from 45 different client‐owned dogs were spiked with dalteparin at 9 concentrations with increasing anti‐factor Xa (anti‐FXa) activity. Activated partial thromboplastin time, tissue factor‐induced TG, and anti‐FXa activity were measured for each concentration. Concentration–response relationships were determined with ADAPT v.5, using various nonlinear regression models for stimulatory or inhibitory effects. Results Thrombin generation ranges of client‐owned dogs and Beagles were equivalent only for time‐to‐peak (P < .05). In vitro dalteparin resulted in a concentration‐dependent decrease in endogenous thrombin potential (ETP) in pooled PPP. The estimated dalteparin concentration that produced half the maximal inhibition of baseline ETP (IC50) was 0.289 U/mL. Thrombin generation and anti‐FXa activity were more sensitive than APTT to detect the effects of dalteparin. Conclusions The CAT assay can measure the effects of dalteparin in canine plasma, resulting in significant dose‐dependent decreases in ETP, prompting further in vivo investigation.
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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) | 0.000 | 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".