Comparing citrated native, kaolin-activated, and tissue factor–activated samples and determining intraindividual variability for feline thromboelastography
Bibliographic record
Abstract
Thromboelastography (TEG) is a point-of-care whole blood test of hemostasis. While TEG is becoming more widely used in veterinary medicine, few studies describe the use of TEG in cats. The objectives of the current study were to: 1) document the range of TEG variables produced in healthy cats using 3 sample types (citrated native, kaolin-activated, and tissue factor-activated), and 2) determine if there was a significant difference between 2 separate samples obtained from individual healthy cats on the same day. Jugular venipuncture was performed in 20 cats, and citrated blood collected for TEG. TEG analysis was performed on citrated native, kaolin-activated, and tissue factor-activated blood for each sample. Two hours later, the procedure was repeated from the opposite jugular vein, yielding a total of 120 analyses. Reaction time (R), alpha angle (α), kappa value (κ), and maximum amplitude (MA) were recorded from each tracing. No significant differences were found between TEG tracings from the first and second venipuncture samples. Significant differences were found between sample types for R, α, κ, and MA. Means for citrated native/kaolin-activated/tissue factor-activated methods were R = 4.1/3.7/0.6 min; κ = 2.5/1.8/2.2 min; α = 59.9/65.1/70.4 degrees; MA = 47.4/49.9/44.7 mm. A limitation of this study was the small number of cats used. Thromboelastography analysis may be a suitable method of evaluating hemostasis in cats.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".