Heparinase‐modified thromboelastography in cats
Bibliographic record
Abstract
BACKGROUND: Evaluation of underlying hemostatic function is challenging when feline patients are receiving an anticoagulant medication. Discontinuing the anticoagulant to obtain accurate results for hemostatic testing may lead to thrombotic complications. The addition of heparinase to blood samples may mitigate the effects of exogenous heparin and allow hemostatic testing. METHODS: Tissue factor (TF)-activated thromboelastography (TEG) was performed using citrated whole blood from 19 cats. Assays were performed using citrated whole blood, with and without addition of unfractionated heparin to a concentration of 0.2 U/mL. For each blood sample, TEG assays were performed using a standard cup and a heparinase-coated cup. KEY FINDINGS: For TEG variables R, k, α-angle, and MA, mean values were not statistically different when citrated blood was used with standard or heparinase-coated cups. Heparinized blood assayed in standard cups displayed a significantly increased R and k, and significantly decreased α-angle and MA when compared to heparinized blood assayed in heparinase-coated cups. TEG variables for heparinized blood assayed in heparinase cups was not statistically different from those of the citrated whole blood without added heparin. SIGNIFICANCE: Heparinase modified, TF-activated, TEG reverses heparin effects in feline-citrated blood.
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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 | 0.001 | 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) | 0.001 | 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".