Bibliographic record
Abstract
With the advent of multislice computed tomography (CT) and advanced magnetic resonance (MR) angiography protocols, cross-sectional imaging has become a gold standard diagnostic technique for hepatovascular anomalies. This chapter focuses on various vascular disorders, including arterioportal fistula, congenital intrahepatic shunts, multiple acquired extrahepatic shunts, congenital extrahepatic shunts and complex vascular anomalies. MR angiography has been used for diagnosis of hepatovascular disorders because of its excellent depiction of the portal and abdominal vasculature. Congenital intrahepatic shunts occur in large-breed dogs (Irish Wolfhounds, Golden Retrievers, Labrador Retrievers, Australian Cattle Dogs, Old English Sheepdogs) and rarely in cats, resulting in a large-diameter direct communication with the caudal vena cava. Multiple acquired extrahepatic portosystemic shunts form because of portal hypertension, often due to primary hepatic parenchymal disease. Congenital extrahepatic shunts occur in smaller-breed dogs (Cairn Terriers, Yorkshire Terriers, Russell Terriers, Dachshunds, Miniature Schnauzers, Maltese) and cats.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.005 |
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".