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Although robotic segmentectomy has been applied for the treatment of small pulmonary lesions for many years, studies on the learning curve of robotic segmentectomy are quite limited. Thus, we aim to investigate the learning curve of robotic portal segmentectomy with 4 arms (RPS-4) using prospectively collected data in patients with small pulmonary lesions. One hundred consecutive patients with small pulmonary lesions who underwent RPS-4 between June 2018 and April 2021 were included in the study. Da Vinci Si/Xi systems were used to perform RPS-4. The mean operative time, console time, and docking time for the entire cohort were 119.2 ± 41.6, 85.0 ± 39.6, and 6.6 ± 2.8 min, respectively. The learning curve of RPS-4 can be divided into three different phases: 1-37 cases (learning phase), 38-78 cases (plateau phase), and > 78 cases (mastery phase). Moreover, 64 cases were required to ensure acceptable surgical outcomes. The total operative time (P < 0.001), console time (P < 0.001), blood loss (P < 0.001), and chest tube duration (P = 0.014) were reduced as experience increased. In conclusion, the learning curve of RPS-4 could be divided into three phases. 37 cases were required to pass the learning phase, and 78 cases were needed to truly master this technique.
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http://dx.doi.org/10.1007/s11701-023-01545-7 | DOI Listing |
Am J Emerg Med
September 2025
University of Toronto, Rotman School of Management, Canada.
Study Objective: Accurately predicting which Emergency Department (ED) patients are at high risk of leaving without being seen (LWBS) could enable targeted interventions aimed at reducing LWBS rates. Machine Learning (ML) models that dynamically update these risk predictions as patients experience more time waiting were developed and validated, in order to improve the prediction accuracy and correctly identify more patients who LWBS.
Methods: The study was deemed quality improvement by the institutional review board, and collected all patient visits to the ED of a large academic medical campus over 24 months.
PLoS One
September 2025
Neck-Shoulder and Lumbocrural Pain Hospital of Shandong First Medical University, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, China.
Background: Metabolic syndrome (MetS) and sarcopenia are major global public health problems, and their coexistence significantly increases the risk of death. In recent years, this trend has become increasingly prominent in younger populations, posing a major public health challenge. Numerous studies have regarded reduced muscle mass as a reliable indicator for identifying pre-sarcopenia.
View Article and Find Full Text PDFJMIR Med Inform
September 2025
Department of Radiology, Air Force Medical Center, Air Force Medical University, Fucheng Road 30, Haidian District, Beijing, CN.
Background: Lateral malleolar avulsion fracture (LMAF) and subfibular ossicle (SFO) are distinct entities that both present as small bone fragments near the lateral malleolus on imaging, yet require different treatment strategies. Clinical and radiological differentiation is challenging, which can impede timely and precise management. On imaging, magnetic resonance imaging (MRI) is the diagnostic gold standard for differentiating LMAF from SFO, whereas radiological differentiation on computed tomography (CT) alone is challenging in routine practice.
View Article and Find Full Text PDFInt J Cardiovasc Imaging
September 2025
Klinikum Fürth, Friedrich-Alexander-University Erlangen- Nürnberg, Fürth, Germany.
Myocarditis is an inflammation of heart tissue. Cardiovascular magnetic resonance imaging (CMR) has emerged as an important non-invasive imaging tool for diagnosing myocarditis, however, interpretation remains a challenge for novice physicians. Advancements in machine learning (ML) models have further improved diagnostic accuracy, demonstrating good performance.
View Article and Find Full Text PDFJ Neurooncol
September 2025
Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, 266003, Shandong, China.
Rationale And Objectives: Double expression lymphoma (DEL) is an independent high-risk prognostic factor for primary CNS lymphoma (PCNSL), and its diagnosis currently relies on invasive methods. This study first integrates radiomics and habitat radiomics features to enhance preoperative DEL status prediction models via intratumoral heterogeneity analysis.
Materials And Methods: Clinical, pathological, and MRI imaging data of 139 PCNSL patients from two independent centers were collected.