CLINICAL SIGNIFICANCE OF RENAL PELVIC DILATATION ON ULTRASOUND IN DOGS AND CATS
Bibliographic record
Abstract
Renal pelvic dilatation is often recognized sonographically in dogs and cats, but ranges of measurements expected with different urologic conditions remain unknown. Ultrasound images of 81 dogs and 66 cats with renal pelvic dilatation were reviewed, and six groups were formed based on medical records: (I) clinically normal renal function, and (II) clinically normal renal function with diuresis; (III) pyelonephritis; (IV) noninfectious renal insufficiency; (V) outflow obstruction; (VI) miscellaneous nonobstructive anomalies. Medians for maximal pelvic width (range) for group I was 2.0 mm (1.0-3.8) in 11 dogs, and 1.6 mm (0.8-3.2) in 10 cats; for group II, 2.5 mm (1.3-3.6) in 15 dogs, and 2.3mm (1.1-3.4) in 16 cats; for group III, 3.6 mm (1.9-12.0) in nine dogs, and 4.0 mm (1.7-12.4) in seven cats; for group IV, 3.1 mm (0.5-10.8) in 33 dogs, and 2.8 mm (1.2-7.3) in 13 cats; for group V, 15.1mm (5.1-76.2) in six dogs, and 6.8mm (1.2-39.1) in 17 cats; and for group VI, 3.8mm (1.2-7.6) in seven dogs, and 3.0 mm (1.3-7.5) in three cats. Pelvic width in group I was lower than in groups III-V (P = 0.0001), but did not significantly differ from group II. Pelvic width > or =13 mm always indicated obstruction. While the proportion of bilateral pelvic dilatation was not different among groups, the difference in pelvic width (maximal-minimal) was greater in group V vs. groups I, II, and IV (P = 0.0009). These results confirm that renal pelvic dilatation can be detected sonographically in dogs and cats with clinically normal renal function, and that it increases with renal insufficiency, pyelonephritis, or outflow obstruction. Nevertheless, renal pelvic width varies substantially within groups and should be interpreted with caution.
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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 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".