Evaluation of platelet function in dogs with cardiac disease using the PFA‐100 platelet function analyzer
Bibliographic record
Abstract
BACKGROUND: Cardiac disease has the potential to alter platelet function in dogs. Evaluation of platelet function using the PFA-100 analyzer in dogs of multiple breeds and with a broad range of cardiac conditions would help clarify the effect of cardiac disease on platelets. OBJECTIVES: The objective of this study was to assess differences in closure time (CT) in dogs with cardiac disease associated with murmurs, when compared with that of healthy dogs. METHODS: Thirty-nine dogs with cardiac murmurs and turbulent blood flow as determined echocardiographically were included in the study. The dogs represented 23 different breeds. Dogs with murmurs were further divided into those with atrioventricular valvular insufficiency (n=23) and subaortic stenosis (n=9). Fifty-eight clinically healthy dogs were used as controls. CTs were determined in duplicate on a PFA-100 analyzer using collagen/ADP cartridges. RESULTS: Compared with CTs in the control group (mean+/-SD, 57.6+/-5.9 seconds; median, 56.5 seconds; reference interval, 48.0-77.0 seconds), dogs with valvular insufficiency (mean+/-SD, 81.9+/-26.3 seconds; median, 78.0 seconds; range, 52.5-187 seconds), subaortic stenosis (71.4+/-16.5 seconds; median, 66.0 seconds; range, 51.5-95.0 seconds), and all dogs with murmurs combined (79.6+/-24.1 seconds; median, 74.0 seconds; range, 48.0-187 seconds) had significantly prolonged CTs (P<.01). CONCLUSIONS: The PFA-100 analyzer is useful in detecting platelet function defects in dogs with cardiac murmurs, most notably those caused by mitral and/or tricuspid valvular insufficiency or subaortic stenosis. The form of turbulent blood flow does not appear to be an important factor in platelet hypofunction in these forms of cardiac disease.
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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 | 0.000 |
| 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".