Biologic variability and correlation of platelet function testing in healthy dogs
Bibliographic record
Abstract
BACKGROUND: Platelet function tests are influenced by biologic variability, including inter-individual (CVG ) and intra-individual (CVI ), as well as analytic (CVA ) variability. Variability in canine platelet function testing is unknown, but if excessive, would make it difficult to interpret serial results. Additionally, the correlation between platelet function tests is poor in people, but not well described in dogs. OBJECTIVES: The aims were to: (1) identify the effect of variation in preanalytic factors (venipuncture, elapsed time until analysis) on platelet function tests; (2) calculate analytic and biologic variability of adenosine diphosphate (ADP) and arachidonic acid (AA)-induced thromboelastograph platelet mapping (TEG-PM), ADP-, AA-, and collagen-induced whole blood platelet aggregometry (WBA), and collagen/ADP and collagen/epinephrine platelet function analysis (PFA-CADP, PFA-CEPI); and (3) determine the correlation between these variables. METHODS: In this prospective observational trial, platelet function was measured once every 7 days, for 4 consecutive weeks, in 9 healthy dogs. In addition, CBC, TEG-PM, WBA, and PFA were performed. RESULTS: Overall coefficients of variability ranged from 13.3% to 87.8% for the platelet function tests. Biologic variability was highest for AA-induced maximum amplitude generated during TEG-PM (MAAA; CVG = 95.3%, CVI = 60.8%). Use of population-based reference intervals (RI) was determined appropriate only for PFA-CADP (index of individuality = 10.7). There was poor correlation between most platelet function tests. CONCLUSIONS: Use of population-based RI appears inappropriate for most platelet function tests, and tests poorly correlate with one another. Future studies on biologic variability and correlation of platelet function tests should be performed in dogs with platelet dysfunction and those treated with antiplatelet therapy.
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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 | 0.003 | 0.007 |
| 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 | 0.000 | 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".