Publications by authors named "A Alanay"
Purpose: Appendicular skeleton markers are commonly used for maturity assessment for Adolescent Idiopathic Scoliosis (AIS) patients. Traditionally, Risser has been a standard skeletal maturity assessment method. More recently, Sanders classification (SSMS), as a more comprehensive system, became popular, especially in decision-making for Vertebral Body Tethering (VBT).
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- * Methods: Researchers developed 10 critical questions from frequently asked AIS inquiries and had the chatbots respond, then evaluated the accuracy, clarity, and empathy of the answers using a rating system by experienced spine surgeons, while also gathering opinions on AI in healthcare.
- * Results: ChatGPT 4.0 performed the best with 39% 'excellent' ratings, while overall, only 26% of responses were rated 'excellent.' Not
Article Synopsis
- The study aims to determine how rod characteristics, screw density, and cages affect mechanical complications in spinal surgeries compared to patient-related factors and alignment in adult spinal deformity cases.
- Data from 302 patients was analyzed using different statistical models to measure outcomes like pseudarthrosis and screw loosening and understand the impact of surgical techniques versus patient demographics.
- Key findings include that using four rods reduces the risk of pseudarthrosis, postoperative malalignment significantly increases the risk of complications, and high screw density leads to a lower risk of screw loosening, while age has a secondary impact on outcomes.
Article Synopsis
- This study analyzed data from multiple centers to explore how machine learning can help in predicting treatment strategies (surgery vs. conservative) for patients with adult spine deformity (ASD).
- It found that patients whose initial treatment approach matched the machine learning predictions were more likely to reach meaningful clinical improvement, measured by the Minimum Clinically Important Difference (MCID) in various health indices.
- The results indicated that different patient clusters (based on conditions like scoliosis and sagittal imbalance) showed varying success rates in achieving MCID, emphasizing the importance of accurate treatment predictions in improving patient outcomes.