Computed tomography with Hounsfield unit assessment is useful in the diagnosis of liver lobe torsion in pet rabbits (<i>Oryctolagus cuniculus</i>)
Bibliographic record
Abstract
Clinical signs of liver lobe torsion in rabbits are often nonspecific and mimic those that are also generally detected with gastrointestinal stasis. Nonspecific clinical signs may result in pursuit of full-body imaging such as computed tomography (CT). The aim of this multicenter, retrospective, case series study was to describe CT findings of liver lobe torsion in a group of rabbits. Computed tomography studies of six rabbits with confirmed liver lobe torsion by surgery or necropsy were evaluated. The caudate liver lobe was affected in six out of six rabbits and was enlarged, rounded, hypoattenuating, heterogeneous, and minimally to noncontrast enhancing, with scant regional peritoneal effusion. Precontrast, mean Hounsfield units (HU) of the torsed liver lobe (39.3 HU [range, 24.4-48.1 HU]) were lower than mean HU of normal liver (55.1 HU [range, 49.6-60.8 HU]), with a mean torsed:normal HU ratio of 0.71 (range, 0.49-0.91). Postcontrast, mean HU of the torsed liver lobe (38.4 HU [range, 19.7-48.9 HU]) were also lower than mean HU of normal liver (108.4 HU [range, 84.5-142.0 HU]), with a lower postcontrast mean torsed:normal HU ratio of 0.35 (range, 0.14-0.48) compared to precontrast. Mean HU of torsed liver lobes had little difference pre- and postcontrast (postcontrast HU 1.0 times the average precontrast HU [range, 0.81-1.1]), and contrast enhancement of the torsed liver lobes was on average 50% lower than in normal liver. Liver lobe torsion should be considered in rabbits with an enlarged, hypoattenuating, heterogeneous, minimally to noncontrast enhancing liver lobe, particularly the caudate lobe, and scant regional peritoneal effusion.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".