Feasibility of laparoscopic liver lobectomy in dogs
Bibliographic record
Abstract
OBJECTIVE: To determine the feasibility of laparoscopic liver lobectomy (LLL) in dogs by using canine cadavers and to describe the clinical application in dogs with liver disease. STUDY DESIGN: Ex vivo experiment and descriptive case series. SAMPLE POPULATION: Twelve canine cadavers and six client-owned dogs. METHODS: Cadavers underwent LLL with an endoscopic stapler. The percentage of liver lobe resected was determined by volume. The distance from the staple line to hilus was measured. Medical records of dogs undergoing LLL were reviewed. RESULTS: In cadavers ≤15 kg, left lateral lobectomy completeness was 87.3% (84.6%-96.6%), and remaining median (interquartile range) hilar length was 1 cm (0.25-1.75). Left medial lobectomy completeness was 72.5% (66.7%-80%), and remaining hilar length was 1.6 cm (0.47-1.75). Central division resection completeness was 68.3% (60%-92.9%), and remaining hilar length was 2.7 cm (0.8-5). Laparoscopic liver lobectomy was not feasible for right division lobes and in cadavers >15 kg. Five dogs with peripheral quadrate or left lateral lobe masses underwent stapled, partial laparoscopic lobectomy (30%-90%). One dog underwent stapled, left lateral lobectomy (90%) after open procedure conversion. Histopathological diagnoses included hepatocellular carcinoma (3), nodular hyperplasia (1), biliary cyst adenoma (1), and fibrosis (1). CONCLUSION: Laparoscopic liver lobectomy of the left and central divisions is feasible in cadavers ≤15 kg with an endoscopic stapler. Partial LLL of the left and central divisions is feasible in select dogs with liver disease. CLINICAL SIGNIFICANCE: Laparoscopic liver lobectomy may be a viable alternative to laparotomy in small-to-medium size dogs with peripheral liver masses of the left and central divisions.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".