COMPUTED TOMOGRAPHY FEATURES OF DIVISIONAL BILE DUCTS IN HEALTHY LABRADOR RETRIEVERS
Bibliographic record
Abstract
Gallbladder and bile ducts pathology are common in dogs and typically investigated with ultrasonography. Abdominal computed tomography (CT) is becoming more and more popular especially in large dogs or with challenging diagnoses. The first step in interpreting CT findings is the knowledge of the cross-sectional anatomy of the involved structures. Detailed bile ducts anatomy is reported in canine cadavers [1][2], however detailed studies describing some CT biliary tract features are lacking in the veterinary literature, especially the divisional ducts anatomy draining the individual divisions of the liver in to the common bile duct. In dogs, the most common pattern includes 3 (2 central and 1 right or left) or 4 (2 central + 1 left and 1 right) divisional ducts. The aim of this retrospective descriptive study was to evaluate the visibility, size and pattern of the divisional ducts in contrast-enhanced CT (CECT) sequences in a group of adult Labrador Retriever without evidence of hepatobiliary diseases. Moreover, the correlation with the visceral fat area (VFA%) was evaluated.
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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".