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Redefining supplier selection in healthcare sector: a novel framework for supplier classification.
J Health Organ Manag
January 2025
Amrita School of Business - Amritapuri Kollam Campus, Kollam, India.
Purpose: This paper aims to delve into the critical aspect of supplier selection in the healthcare sector, emphasizing the significance of strategic sourcing in enhancing operational efficiency and quality of services. The primary aim is to develop a comprehensive framework for supplier evaluation that aligns with the unique requirements of hospitals, ultimately improving procurement processes and patient care outcomes.
Design/methodology/approach: The study leverages the renowned Carter's 7 C model as a foundational framework for supplier assessment, supplemented by insights gathered from interviews with experts in the New Product Introduction, Purchasing and Procurement departments of a leading hospital in India.
Integrating Retinal Segmentation Metrics with Machine Learning for Predictions from Mouse SD-OCT Scans.
Curr Eye Res
January 2025
Department of Ophthalmology, Edward S. Harkness Eye Institute, Columbia University, Vagelos College of Physicians and Surgeons, New York, NY, USA.
Purpose: This study aimed to initially test whether machine learning approaches could categorically predict two simple biological features, mouse age and mouse species, using the retinal segmentation metrics.
Methods: The retinal layer thickness data obtained from C57BL/6 and DBA/2J mice were processed for machine learning after segmenting mouse retinal SD-OCT scans. Twenty-two models were trained to predict the mouse groups.
Effectiveness and safety of biosimilars in pediatric inflammatory bowel diseases: an observational longitudinal study on the French National Health Data System.
World J Pediatr
January 2025
EPI-PHARE, French National Agency for Medicines and Health Products Safety (ANSM) and French National Health Insurance (CNAM), 143-147 Boulevard Anatole France, 93285, Saint-Denis, France.
Background: Data on biosimilar use in pediatric inflammatory bowel diseases (IBD) are scarce compared to the status of studies in adults, resulting in limitations in its treatment. We compared effectiveness and safety of biosimilars versus originators in this population.
Methods: We used data from the French National Health Data System to identify children (less than 18 years old at treatment initiation) initiating treatment with a biosimilar or the originator infliximab or adalimumab for Crohn's disease (CD) or ulcerative colitis (UC), from first biosimilar launch (January 2015 and October 2018, respectively) to 31 December 2022.
Fixation-related potentials during a virtual navigation task: The influence of image statistics on early cortical processing.
Atten Percept Psychophys
January 2025
U.S. DEVCOM Army Research Laboratory, Humans in Complex Systems, Aberdeen Proving Ground, MD, USA.
Historically, electrophysiological correlates of scene processing have been studied with experiments using static stimuli presented for discrete timescales where participants maintain a fixed eye position. Gaps remain in generalizing these findings to real-world conditions where eye movements are made to select new visual information and where the environment remains stable but changes with our position and orientation in space, driving dynamic visual stimulation. Co-recording of eye movements and electroencephalography (EEG) is an approach to leverage fixations as time-locking events in the EEG recording under free-viewing conditions to create fixation-related potentials (FRPs), providing a neural snapshot in which to study visual processing under naturalistic conditions.
View Article and Find Full Text PDFPerformance of Radiomics-based machine learning and deep learning-based methods in the prediction of tumor grade in meningioma: a systematic review and meta-analysis.
Neurosurg Rev
January 2025
Department of Neurosurgery, Mount Sinai Hospital, Icahn School of Medicine, New York City, NY, USA.
Currently, the World Health Organization (WHO) grade of meningiomas is determined based on the biopsy results. Therefore, accurate non-invasive preoperative grading could significantly improve treatment planning and patient outcomes. Considering recent advances in machine learning (ML) and deep learning (DL), this meta-analysis aimed to evaluate the performance of these models in predicting the WHO meningioma grade using imaging data.
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