Comparison of Different Sampling Methods on Viscoelastic Test Results Using a Point‐of‐Care Coagulation Monitor in Healthy Dogs
Bibliographic record
Abstract
BACKGROUNDS: Studies investigating the influence of sampling methods on point-of-care viscoelastic test (VCM Vet) results are limited. OBJECTIVES: Investigating the impact of blood sampling methods on VCM Vet results in dogs, and determining if results are affected by hematological parameters, and blood sampling difficulty. METHODS: Two VCM assays were performed on 52 healthy dogs. Blood sample was first collected from a direct jugular venipuncture on all dogs to run a baseline VCM Vet assay, perform a CBC, and measure fibrinogen concentration. A second VCM Vet assay was performed one hour later with blood sampling methods randomized as follows: contralateral jugular using a vacutainer, direct stick in a saphenous vein, or blood sampling via a cephalic intravenous catheter. RESULTS: Reference intervals were established for each VCM Vet parameter with the first blood sample. The intra-class correlation (ICC) between sampling methods was poor (< 0.5). There was a weak positive correlation between hematocrit and CT (p < 0.042), a weak negative correlation between platelet count and CFT (p < 0.01), and a weak positive correlation between platelet count and alpha (p = 0.002), A10 (p < 0.001), A20 (p < 0.001), and MCF (p < 0.001). CONCLUSIONS: Sampling protocols influence VCM Vet results. Each sampling method is reliable but not correlated. Follow-up on a patient should be performed using the same sampling method and site. CBC results should be known before interpreting results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".