In this episode of the Simini Small Animal Surgery Podcast, we explore two critical and very different questions facing small animal surgeons today: How do we better predict neurologic recovery in deep pain-negative dogs? And how can we optimize pulmonary staple lines to reduce air leaks?

We cover two impactful studies:

✅ Low et al. — A multicenter study that trained and validated a machine learning model (XGBoost) to predict post-op ambulation in dogs with acute TL-IVDE. The model outperformed traditional markers and could change how we counsel and select cases for surgery.

✅ Huerta et al. — An ex vivo investigation into staple line air leakage across various lobectomy techniques. The results? Total lobectomies were far more robust, while partial techniques—especially wedge resections—leaked at physiologic pressures.

Whether you're refining surgical counseling or checking your staple lines more thoroughly, this episode brings data-driven insight to daily decision-making.

🎓 Journal Articles Discussed:

• Low et al. — Machine-learning-based prediction of functional recovery in deep-pain-negative dogs after decompressive thoracolumbar hemilaminectomy for acute intervertebral disc extrusion
• Huerta et al. — Leakage pressures of partial and total lung lobectomies performed with thoracoabdominal staplers in cadaveric dogs
  📚 From the May 2025 issue of Veterinary Surgery

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