Quantification of hepatic and renal cortical echogenicity in clinically normal cats
Bibliographic record
Abstract
OBJECTIVE: To quantitatively determine echogenicity of the liver and renal cortex in clinically normal cats. ANIMALS: 17 clinically normal adult cats. PROCEDURE: 3 ultrasonographic images of the liver and the right kidney were digitized from video output from each cat. Without changing the ultrasound machine settings, an image of a tissue-equivalent phantom was digitized. Biopsy specimens of the right renal cortex and liver were obtained for histologic examination. Mean pixel intensities within the region of interest (ROI) on hepatic, renal cortical, and tissue-equivalent phantom ultrasonographic images were determined by histogram analysis. From ultrasonographic images, mean pixel intensities for hepatic and renal cortical ROI were standardized by dividing each mean value by the mean pixel intensity from the tissue-equivalent phantom. RESULTS: The mean (+/- SD) standardized hepatic echogenicity value was 1.06 +/- 0.02 (95% confidence interval, 1.02 to 1.10). The mean standardized right renal cortical echogenicity value was 1.04 +/- 0.02 (95% confidence interval, 1.01 to 1.08). The mean combined standardized hepatic and renal cortical echogenicity value was 1.02 +/- 0.05 (95% confidence interval, 0.99 to 1.04). CONCLUSIONS AND CLINICAL RELEVANCE: Quantitative determination of hepatic and renal cortical echogenicity in cats is feasible, using histogram analysis, and may be useful for early detection of diffuse parenchymal disease and for serially evaluating disease progression.
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 | 0.002 |
| 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".