Video‐assisted thoracic surgery for the management of pyothorax in dogs: 14 cases
Bibliographic record
Abstract
OBJECTIVE: To report the perioperative findings and outcome of dogs undergoing video-assisted thoracic surgery (VATS) for the management of pyothorax. DESIGN: Multi-institutional, retrospective study. ANIMALS: Client-owned dogs (n = 14). METHODS: Medical records of dogs with pyothorax managed via VATS were reviewed for signalment, history, clinical signs, clinicopathological findings, diagnostic imaging results, surgical variables, bacterial culture and sensitivity results, post-operative management and outcome. VATS was performed after placing a paraxyphoid endoscopic portal and 2-3 intercostal instrument portals. VATS exploration was followed by one or more of the following: mediastinal debridement, tissue sampling, pleural lavage, and placement of a thoracostomy tube. RESULTS: Two dogs (14%) required conversion from VATS to an open thoracotomy to completely resect proliferative mediastinal tissue. These dogs had severe pleural effusion on preoperative thoracic radiographs and one had severely thickened contrast-enhancing mediastinum on preoperative computed tomography (CT). The cause of pyothorax was identified as a penetrating gastric foreign body (n = 2), migrating plant material (n = 2), and idiopathic (n = 10). The median follow-up time was 143 days (range, 14-2402 days). All dogs were discharged from the hospital and their clinical signs resolved. One patient had recurrence of a pyothorax requiring revision surgery 17 months postoperatively. CONCLUSION: VATS allows minimally invasive treatment of uncomplicated canine pyothorax. Preoperative thoracic CT may help identify candidates for VATS among dogs with pyothorax.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".