COMPUTED TOMOGRAPHIC CHARACTERISTICS OF COLLATERAL VENOUS PATHWAYS IN DOGS WITH CAUDAL VENA CAVA OBSTRUCTION
Bibliographic record
Abstract
Collateral venous pathways develop in dogs with obstruction or increased blood flow resistance at any level of the caudal vena cava in order to maintain venous drainage to the right atrium. The purpose of this retrospective study was to describe the sites, causes of obstruction, and configurations of venous collateral pathways for a group of dogs with caudal vena cava obstruction. Computed tomography databases from two veterinary hospitals were searched for dogs with a diagnosis of caudal vena cava obstruction and multidetector row computed tomographic angiographic (CTA) scans that included the entire caudal vena cava. Images for each included dog were retrieved and collateral venous pathways were characterized using image postprocessing and a classification system previously reported for humans. A total of nine dogs met inclusion criteria and four major collateral venous pathways were identified: deep (n = 2), portal (n = 2), intermediate (n = 7), and superficial (n = 5). More than one collateral venous pathway was present in 5 dogs. An alternative pathway consisting of renal subcapsular collateral veins, arising mainly from the caudal pole of both kidneys, was found in three dogs. In conclusion, findings indicated that collateral venous pathway patterns similar to those described in humans are also present in dogs with caudal vena cava obstruction. These collateral pathways need to be distinguished from other vascular anomalies in dogs. Postprocessing of multidetector-row CTA images allowed delineation of the course of these complicated venous pathways and may be a helpful adjunct for treatment planning in future cases.
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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 | 0.002 |
| 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".