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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.013 |
| 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 | 0.001 | 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".