Evaluation of Video‐Assisted Thoracic Surgery for Treatment of Spontaneous Pneumothorax and Pulmonary Bullae in Dogs
Bibliographic record
Abstract
OBJECTIVE: To describe the operative findings and clinical outcome in dogs undergoing video-assisted thoracic surgery (VATS) for treatment of spontaneous pneumothorax and pulmonary bullae. STUDY DESIGN: Multi-institutional retrospective case series. ANIMALS: Dogs (n = 12) with spontaneous pneumothorax and/or pulmonary bullae. METHODS: Medical records (2008-2013) were reviewed for signalment, clinical signs, diagnostic imaging, surgical and histopathologic findings, and outcome in 12 dogs that had VATS for treatment of spontaneous pneumothorax and pulmonary bullae. In particular, conversion to median sternotomy and surgical success were evaluated. RESULTS: Twelve dogs had initial VATS for spontaneous pneumothorax and/or pulmonary bullae. Conversion to median sternotomy because of inability to identify a parenchymal lesion/leak was necessary in 7 (58%) dogs. VATS without conversion to median sternotomy was performed in 6 (50%) dogs. Successful surgical outcomes occurred in 5 (83%) dogs that had conversion to median sternotomy, and in 3 (50%) dogs that had VATS without conversion to median sternotomy. CONCLUSIONS: Exploratory thoracoscopy was associated with a high rate of conversion to median sternotomy because of inability to identify leaking pulmonary lesions in dogs with spontaneous pneumothorax and pulmonary bullae. Failure to convert to a median sternotomy may be associated with recurrent or persistent pneumothorax.
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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 | 0.001 |
| Scholarly communication | 0.001 | 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".