Effects of size and location of regions of interest examined by use of contrast-enhanced ultrasonography on renal perfusion variables of dogs
Bibliographic record
Abstract
OBJECTIVE To determine effects of the size and location of regions of interest (ROIs) in the renal cortex of unsedated dogs on renal perfusion variables determined by use of contrast-enhanced ultrasonography (CEUS). ANIMALS 12 client-owned adult (1.5 to 2 years old) Labrador Retrievers (8 males and 4 females; mean ± SD body weight, 27 ± 1.6 kg). PROCEDURES Each dog received 2 bolus injections of sulfur hexafluoride during CEUS. Three small oval ROIs (area of each ROI, 0.11 cm(2)) located in a row with a distance of 1 mm between adjacent ROIs and 1 large oval ROI (area, 1 cm(2)) that encompassed the 3 smaller ROIs were manually drawn in the renal cortex. The ROIs were located at a depth of 1.5 to 2.0 cm in the near field of the renal cortex. Software analysis of time-intensity curves within each ROI was used to identify peak enhancement, time to peak enhancement, regional blood flow, and mean transit time. RESULTS The location and size of the ROIs of unsedated dogs did not cause significant differences in the mean values of the renal perfusion variables. CONCLUSIONS AND CLINICAL RELEVANCE The development of CEUS has provided a unique means for visually examining and quantifying tissue perfusion. Results of this study indicated that it was possible to use small or large ROIs during renal CEUS to evaluate renal perfusion in dogs.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".