Purpose: Digital replantation is a technically difficult microsurgery requiring significant surgical skill. The aim of this study was to investigate postoperative outcomes associated with the surgical learning curve for microvascular digital replantation.
Methods: A prospectively maintained surgical database of consecutive patients who underwent digital replantation from 2002 to 2012 was reviewed. All cases were performed by a single surgeon and began immediately after the surgeon's fellowship. A total of 46 patients were identified. Outcomes of digital replantation were tested for association with time since fellowship, total microvascular operative experience, and location and type of injury.
Results: Overall, 38/46 (82.6%) of patients underwent a successful digital replantation. There was a significant difference between survival percentages over the years (p=0.04), with improvement seen over time. Total microvascular experience was significantly associated with successful outcomes (p<0.001). After 100 hours of microvascular experience, there was a significant increase in the survival odds ratio (OR 8.5, 95% CI 1.5-47.9). Crush and thumb injuries were more likely to have detrimental outcomes.
Conclusions: There was marked improvement in replant survival over time, with a significant increase in odds of survival after 100 hours of microvascular experience. One hundred operating hours under the microscope occurred around 2 years in practice for this high-volume surgeon. There is strong evidence that a steep learning curve occurs in microvascular digit replantation surgery.
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http://dx.doi.org/10.7759/cureus.66133 | DOI Listing |
Surg Innov
January 2025
Division of General, Minimally Invasive, and Robotic Surgery, Department of Surgery, University of Illinois at Chicago, Chicago, IL, USA.
Background: Transabdominal pre-peritoneal inguinal hernia repair using the da Vinci Single-Port robot (SP-TAPP) is currently performed in few centers. We aimed to define the learning curve for SP-TAPP by analyzing operative times.
Methods: The operative times of 122 SP-TAPP performed between 2019 and 2024 were retrospectively analyzed.
Alzheimers Res Ther
January 2025
Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Sankt Augustin, Germany.
Background: Alzheimer's disease (AD) is a progressive neurodegenerative disorder affecting millions worldwide, leading to cognitive and functional decline. Early detection and intervention are crucial for enhancing the quality of life of patients and their families. Remote Monitoring Technologies (RMTs) offer a promising solution for early detection by tracking changes in behavioral and cognitive functions, such as memory, language, and problem-solving skills.
View Article and Find Full Text PDFEur J Nucl Med Mol Imaging
January 2025
Department of Nuclear Medicine, Xiangya Hospital, Central South University, No. 87 Xiangya Road, Changsha, Hunan, 410008, P.R. China.
Purpose: To develop and validate a prostate-specific membrane antigen (PSMA) PET/CT based multimodal deep learning model for predicting pathological lymph node invasion (LNI) in prostate cancer (PCa) patients identified as candidates for extended pelvic lymph node dissection (ePLND) by preoperative nomograms.
Methods: [Ga]Ga-PSMA-617 PET/CT scan of 116 eligible PCa patients (82 in the training cohort and 34 in the test cohort) who underwent radical prostatectomy with ePLND were analyzed in our study. The Med3D deep learning network was utilized to extract discriminative features from the entire prostate volume of interest on the PET/CT images.
Sci Rep
January 2025
Imaging Department, Yantaishan Hospital, Yantai, China.
Noise-induced hearing loss (NIHL) is a common occupational condition. The aim of this study was to develop a classification model for NIHL on the basis of both functional magnetic resonance imaging (fMRI) and structural magnetic resonance imaging (sMRI) by applying machine learning methods. fMRI indices such as the amplitude of low-frequency fluctuation (ALFF), fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), degree of centrality (DC), and sMRI indices such as gray matter volume (GMV), white matter volume (WMV), and cortical thickness were extracted from each brain region.
View Article and Find Full Text PDFJMIR Med Inform
January 2025
Department of Gynecology and Obstetrics, West China Second University Hospital, Sichuan University, Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, China.
Background: Postpartum depression (PPD) is a prevalent mental health issue with significant impacts on mothers and families. Exploring reliable predictors is crucial for the early and accurate prediction of PPD, which remains challenging.
Objective: This study aimed to comprehensively collect variables from multiple aspects, develop and validate machine learning models to achieve precise prediction of PPD, and interpret the model to reveal clinical implications.
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