Quantification of Doppler Indices, Contrast Ultrasound Enhancement Phases and Perfusion Parameters of Splenic Parenchyma in Healthy Dogs
Bibliographic record
Abstract
Background: The spleen can be affected by a variety of vascular problems. Affected tissue neovascularization in these conditions can be effectively analysed using imaging modalities such as Doppler and contrast enhanced ultrasonography (CEUS). Methods: Nine healthy dogs were included in this clinical study comprising two breeds of Labrador Retrievers, German Shepherds, and Beagles and one breed each of Crossbred, Pug, and Pitbulls. Pulsed-wave Doppler indices and contrast-enhanced perfusion parameters of the splenic vasculature were obtained by performing spectral Doppler sonography and CEUS using a second-generation ultrasound contrast agent (Sonovue). Result: The mean ± SE value of splenic vein’s Doppler indices were 10.94±2.04 cm/s (PSV), 6.92±1.28 cm/s (EDV), 6.44±1.57 cm/s (MV), 0.35±0.05 (RI) and 0.75±0.17 (PI), while the indices of splenic artery were 31.21±3.12 cm/s (PSV), 8.95±1.63 cm/s (EDV), 13.98±1.95 cm/s (MV), 0.71±0.04 (RI), 1.75± 0.24 (PI). Arterial and venous phases were visible on a CEUS of the splenic parenchyma. The mean ± SE value of the contrast enhanced splenic perfusion parameters (measured in seconds) were as follows: arrival time = 7.00 ± 0.33; time to initial peak = 15.05 ± 0.46; time to final peak = 28.92±1.06; decline time = 97.89±2.82; washout time = 152.22± 9.10.
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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 | 0.000 | 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".