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The robotic NICE procedure is a novel minimally invasive approach in colorectal surgery, yet its learning curve remains undefined; this study aimed to define its distinct phases by analyzing operative time trends. A retrospective review of 170 consecutive NICE procedures performed by a single surgeon between May 2018 and August 2019 was undertaken. Skin-to-skin operative time was the learning curve surrogate and plotted with unadjusted and risk-adjusted cumulative sum (CUSUM) analyses. Risk adjustment incorporated age, body mass index, ASA class, sex, prior abdominal surgery, diagnosis, anastomotic level, and the need for diverting loop ileostomy-used as a marker of case complexity rather than a direct time determinant. Phase-specific peri-operative outcomes were compared non-parametrically. Five proficiency phases were identified: Initial Learning (cases 1-10), Experienced (11-65), Second Learning (66-80), Advanced Experienced (81-124) and Mastery (125-170). The unadjusted CUSUM rose to + 342 min above the cohort mean by case 10, crossed the baseline at case 55, then gradually declined and plateaued at approximately -215 min by case 170. Risk-adjusted CUSUM displayed parallel inflection points (peak + 78 min, nadir -162 min), confirming that an easier case mix did not explain efficiency gains. Mean operative time fell from 248.8 ± 64.1 min in the first phase to approximately 185 min in Mastery, and inter-quartile variability narrowed from 125 to 59 min. Intraoperative complications (overall 12.4%), blood loss, organ/space surgical site infection, and 30-day morbidity did not differ across phases (p > 0.05). The robotic NICE procedure follows a five-phase learning curve, with true proficiency-and a 20% reduction in operative time-achieved after ~ 55 cases. Safety metrics remained similar across phases, confirming that efficiency gains reflect skill acquisition rather than easier case mix. These milestones can guide training for surgeons adopting the technique.
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http://dx.doi.org/10.1007/s11701-025-02467-2 | DOI Listing |
J R Coll Physicians Edinb
September 2025
Division of Infectious Diseases, Department of Medicine, National University Hospital, Singapore, Singapore.
Academic publishing is increasingly prevalent in clinical training and practice, as part of the burgeoning field of academic medicine, where physicians are expected not only to perform their conventional clinical duties and responsibilities, but also increasingly have to engage in various forms of scholarly activities to contribute to evidence-based practice, as part of their key performance indicators. However, for physicians who are not trained as academics or scientists, the learning curve for scholarly endeavours can be steep and fraught with setbacks and rejections. Therefore, in this editorial article, we offer our perspectives as residents-in-training on the roles of both clinician-authors and journal editorial/peer review teams in facilitating healthy cognitive-emotional processing of unfavourable manuscript decisions in academic medicine.
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September 2025
Department of Urology, the Second Affiliated Hospital of Kunming Medical University, Kunming, China (H.S., Q.W., S.F., H.W.). Electronic address:
Rationale And Objectives: This study systematically evaluates the diagnostic performance of artificial intelligence (AI)-driven and conventional radiomics models in detecting muscle-invasive bladder cancer (MIBC) through meta-analytical approaches. Furthermore, it investigates their potential synergistic value with the Vesical Imaging-Reporting and Data System (VI-RADS) and assesses clinical translation prospects.
Methods: This study adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.
Heart Rhythm
September 2025
Department of Cardiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland. Electronic address:
Background: The learning curve for pulmonary vein isolation (PVI) using "single-shot" pulsed-field ablation (PFA) is thought to be short. 3D electro-anatomical mapping (3D-EAM) might provide adjunctive information to shorten the learning curve and improve lesion durability.
Objective: To analyze procedural performance markers over time for PVI using PFA and 3D-EAM.
Photodiagnosis Photodyn Ther
September 2025
Department of Ophthalmology, People's Hospital of Feng Jie, Chongqing, 404600, China. Electronic address:
Objective: This study aims to develop a robust, multi-task deep learning framework that integrates vessel segmentation and radiomic analysis for the automated classification of four retinal conditions- diabetic retinopathy (DR), hypertensive retinopathy (HR), papilledema, and normal fundus-using fundus images.
Materials: AND.
Methods: A total of 2,165 patients from eight medical centers were enrolled.
Eur J Radiol
September 2025
Department of Radiology, Affiliated People's Hospital of Jiangsu University, Zhenjiang, Jiangsu 212002, China. Electronic address:
Background: Homogeneous AI assessment is required for CT-T staging of gastric cancer.
Purpose: To construct an End-to-End CT-based Deep Learning (DL) model for tumor T-staging in advanced gastric cancer.
Materials And Methods: A retrospective study was conducted on 460 cases of presurgical CT patients with advanced gastric cancer between 2011 and 2024.