Publications by authors named "G Meccariello"

Article Synopsis
  • The study aimed to gather insights from international experts on the efficacy and challenges of two minimally invasive surgical techniques, transoral laser microsurgery (TLM) and transoral robotic surgery (TORS), for treating supraglottic laryngeal tumors.
  • A survey was conducted with 27 head and neck surgeons, finding that TLM generally took less setup time compared to TORS, although both techniques faced concerns about bleeding during surgery, particularly with TLM.
  • Experts viewed TLM and TORS as largely equal in effectiveness, but noted that TORS offered better control over bleeding and improved visibility during the procedure.
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Article Synopsis
  • A study was conducted on 54 adult patients with submandibular stones who underwent a robotic surgery called sialendoscopy-assisted TORSS between January 2019 and June 2023 to evaluate its safety and effectiveness.
  • The overall success rate of the procedure was 81.5%, with better outcomes for patients with palpable stones compared to those with non-palpable stones (92.7% vs. 46.2%).
  • Results indicated that stone characteristics such as size and location are important for predicting surgical success, emphasizing the need for careful pre-operative planning to select appropriate candidates for the procedure.
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Background: Transoral robotic surgery (TORS) performed after neoadjuvant chemotherapy (NAC) is a promising treatment for advanced-stage oropharyngeal carcinoma (OPSCC) able to reduce the adjuvant therapy administration rate.

Methods: A retrospective bi-centric study was conducted to analyze NAC + TORS versus upfront TORS patients. A 1:1 propensity score matching was used to compare the two groups.

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We present a novel decision-making framework for accelerated degradation tests and predictive maintenance that exploits prior knowledge and experimental data on the system's state. As a framework for sequential decision making in these areas, dynamic programming and reinforcement learning are considered, along with data-driven degradation learning when necessary. Furthermore, we illustrate both stochastic and machine learning degradation models, which are integrated in the framework, using data-driven methods.

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