Optimization of surgical approach for thoracoscopic‐assisted pulmonary surgery in dogs
Bibliographic record
Abstract
OBJECTIVE: To determine the optimal intercostal space (ICS) to perform thoracoscopic-assisted lung lobectomy. STUDY DESIGN: Cadaveric study. ANIMALS: Six mature, medium-sized canine cadavers. METHODS: Cadavers were placed in right or left lateral recumbency. A 15-mm thoracoscopic cannula was inserted in the middle third of the 9th or 10th ICS. A wound retraction device was placed into a 7-cm minithoracotomy incision created in the middle third of the 4th-7th ICS on the left side and the 4th-8th ICS on the right side. The pulmonary ligaments were sectioned by using a combined intracorporeal and extracorporeal technique. Each lung lobe was sequentially withdrawn from the wound retraction device at the respective ICS and side. A thoracoabdominal stapler was positioned to simulate lung lobectomy, and the distance from the stapler anvil to the hilus was measured. RESULTS: Simulated thoracoscopic-assisted lung lobectomy performed at left or right ICS 4 and 5, compared with other ICS evaluated, resulted in a significantly shorter median distance from the stapler anvil to the pulmonary hilus of the left cranial and caudal lung lobes and right cranial and middle lung lobes, respectively (all P < .05). Lobectomy at right ICS 5 or 6 resulted in a significantly shorter median distance from the stapler anvil to the pulmonary hilus of the right caudal and accessory lung lobes, respectively (both P < .05). CONCLUSION: These data may inform minithoracotomy positioning to optimize tumor margin excision during thoracoscopic-assisted lung lobectomy for treatment of pulmonary neoplasia in dogs. CLINICAL SIGNIFICANCE: Complete lung lobectomy is possible by using the described thoracoscopic-assisted technique in normal, cadaveric lungs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".