MULTIDETECTOR‐ROW COMPUTED TOMOGRAPHIC CHARACTERISTICS OF PRESUMED PREURETERAL VENA CAVA IN CATS
Bibliographic record
Abstract
Preureteral vena cava (circumcaval ureter, retrocaval ureter) occurs in a third of the feline population and has been associated with ureteral strictures in humans. The aim of this retrospective cross-sectional study was to describe the contrast-enhanced multidetector row computed tomographic (MDCT) characteristics of presumed preureteral vena cava in a group of cats. Medical records from two institutions located in different continents were searched from 2010-2013 for cases with complete contrast-enhanced MDCT examinations of the abdomen (i.e. included the entire course of the ureters and prerenal and renal segments of the caudal vena cava) and a diagnosis of preureteral caudal vena cava. For cases meeting inclusion criteria, CT scan data were retrieved and characteristics of the preureteral caudal vena cava were recorded. Presence of concomitant renal or ureteral diseases was also recorded. A total of 272 cats had contrast-enhanced abdominal CT scans during the study period and of these, 68 cats (22.43 ± 4.96%) had a diagnosis of presumed preureteral vena cava. In all affected cats, a "reverse-J ureter" was observed, i.e. a ureter running medially at the level of L4-5, passing dorsally to the caudal vena cava and then exiting ventrally between the caudal vena cava and aorta returning to its normal position. Having a preureteral vena cava resulted in an increased risk for concurrent urinary signs (OR = 3.00; CI: 95%; 1.28-6.99; P = 0.01). Findings supported the use of contrast-enhanced MDCT for characterizing morphology of preureteral vena cava and its relation with ureters in cats.
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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 | 0.002 | 0.001 |
| 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".