Video‐Assisted Thoracoscopic Resection of Noninvasive Thymomas Using One‐Lung Ventilation in Two Dogs
Bibliographic record
Abstract
OBJECTIVE: To report the technique and outcome of video-assisted thoracoscopic surgery (VATS) for resection of cranial mediastinal thymoma in 2 dogs. STUDY DESIGN: Case report. ANIMALS: Eleven-year-old Labrador Retrievers (n=2). METHODS: Two dogs had VATS resection of thymoma. Preoperative computed tomography (CT) scans revealed well-circumscribed cranial mediastinal masses with dimensions of 4.5 cm x 4.2 cm x 3.7 cm and 2.1 cm x 2.1 cm x 4.1 cm (at the time of resection) without CT evidence of vascular invasion. One-lung ventilation (OLV) was achieved using a bronchoscopically placed double-lumen endobronchial tube. A 3-portal technique was used for VATS access to the thorax. Thymomas were dissected from the tissues of the cranial mediastinum with the aid of a harmonic scalpel and retrieved in specimen retrieval bags. RESULTS: Two cranial mediastinal thymomas were resected successfully, with their capsules intact, using a VATS technique. One dog developed aspiration pneumonia postoperatively which resolved with treatment and remains healthy 18 months postoperatively with no radiographic evidence of tumor recurrence. The second dog was diagnosed with myasthenia gravis, megaesophagus, and aspiration pneumonia preoperatively and despite recovering well from the procedure had a second episode of aspiration pneumonia 5 days postoperatively and was euthanatized. CONCLUSIONS: VATS resection of modestly sized noninvasive thymoma is possible in dogs. Careful case selection and preoperative imaging are the keys. CLINICAL RELEVANCE: Thoracoscopic resection of modestly sized mediastinal masses forms part of an increasingly expansive set of indications for minimally invasive surgery in select veterinary patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".