Anatomical classification of feline congenital extrahepatic portosystemic shunts based on CT angiography: A SVSTS and VIRIES multi‐institutional study in 231 cats
Bibliographic record
Abstract
The prevalence of anatomical-based subtypes of feline congenital extrahepatic portosystemic shunts (EHPSS) has not been completely elucidated. The goal of this study was to use CT angiography to create an anatomical-based nomenclature system for feline congenital EHPSS. Additionally, subjective portal perfusion scores were generated to determine if intrinsic portal vein development was associated with different shunt conformations or patient age at the time of CT. The SVSTS and VIRIES list services were used to recruit cases. Data collected included patient DOB, gender, breed, weight, CT date, and reported diagnosis. Shunts were classified based upon (1) the shunt portal vessel(s) of origin, (2) the shunt systemic vessel(s) of insertion, and (3) any substantial portal vessels contributing to the shunt. Additionally, hepatic portal perfusion was subjectively scored between 1 (poor/none) and 5 (good/normal) based on the caliber of the intrahepatic PVs. A total of 264 CT scans were submitted from 29 institutions. Due to exclusion criteria, 33 (13%) were removed, leaving 231 CT scans to be included. Twenty-five different EHPSS anatomies were identified with five classifications accounting for 78% of all shunts (LGP [53%], LGC-post [11%], LCG [7%], LGC-pre [4%], and PC [4%]). Shunt origin involved the left gastric vein in 75% of the described classifications. Significant differences were identified among the five most common shunt types with respect to age at the time of CT scan (P = .002), breed (P < .001), and subjective portal perfusion score (P < .001). This refined anatomical classification system for feline EHPSS may enable improved understanding, treatment comparisons, and outcome prediction for cats with these anomalies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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".