Objective: To evaluate how the distribution of patients in groups (based on subjective health experience) changes over time and to investigate differences in physical functioning and mental health between these patient groups.
Design: An observational cohort study.
Setting: University medical center.
Participants: Patients who underwent gastrointestinal or bladder oncological surgery (N=98).
Interventions: Not applicable.
Main Outcome Measures: The classification of patients into different groups based on the subjective health experience model (acceptance and perceived control), preoperatively and 1 and 3 months after discharge.
Results: In total, 98 patients were included. Preoperatively, 31% of the patients were classified as having low acceptance and perceived control (group 4), and this proportion increased to 47% and 45% 1 and 3 months after discharge, respectively. These patients had significantly lower levels of physical functioning (preoperatively, 55 vs 61; =.030; 1 month, 47 vs 57; =.002; 3 months, 52 vs 62; =.006) and higher levels of anxiety and depression (preoperatively, 14 vs 9; <.001; 1 month, 11 vs 3; =.001; 3 months, 10 vs 3; =.009) than patients with high acceptance and perceived control (group 1).
Conclusions: The classification of patients to different groups provides insight in different levels of physical and mental health. However, frequent evaluation is important because of changes in patient groups over time.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11447538 | PMC |
http://dx.doi.org/10.1016/j.arrct.2024.100350 | DOI Listing |
Lung Cancer
January 2025
Dept. of Medical Oncology, Princess Margaret Cancer Center, Toronto, ON, Canada.
Background: Manual extraction of real-world clinical data for research can be time-consuming and prone to error. We assessed the feasibility of using natural language processing (NLP), an AI technique, to automate data extraction for patients with advanced lung cancer (aLC). We assessed the external validity of our NLP-extracted data by comparing our findings to those reported in the literature.
View Article and Find Full Text PDFLung Cancer
January 2025
Internal Medicine III, Wakayama Medical University, Wakayama, Japan.
Objectives: The lack of definitive biomarkers presents a significant challenge for chemo-immunotherapy in extensive-stage small-cell lung cancer (ES-SCLC). We aimed to identify key genes associated with chemo-immunotherapy efficacy in ES-SCLC through comprehensive gene expression analysis using machine learning (ML).
Methods: A prospective multicenter cohort of patients with ES-SCLC who received first-line chemo-immunotherapy was analyzed.
J Geriatr Oncol
January 2025
Hellenic Oncology Research Group (HORG), 55, Lomvardou str, 11470 Athens, Greece.
Introduction: The use of taxanes in the adjuvant setting of early breast cancer (BC) confers survival benefits, however, their role in older patients merits further study. This retrospective pooled analysis of randomized controlled trials conducted by the Hellenic Oncology Research Group (HORG) aims to assess the efficacy and safety of taxane-based adjuvant chemotherapy in older women with BC.
Materials And Methods: Five phase III trials containing a taxane, conducted by HORG between 1995 and 2013, were included in a patient-data pooled analysis.
Acta Pharm
December 2024
Department of Clinical Pharmacy, University Hospital Dubrava, 10000 Zagreb Croatia.
Cardiovascular diseases (CVDs) are the leading cause of mortality and morbidity globally. It is estimated that 17.9 million people died from CVDs in 2019, which represents 32 % of all deaths worldwide.
View Article and Find Full Text PDFCir Cir
January 2025
Department of Neurosurgery, Spinal Health Center, Memorial Hospital, Istanbul, Turkey.
Objective: We aimed to elucidate the histopathological pre-diagnosis of cranial gliomas with magnetic resonance imaging (MRI) techniques in gliomas.
Method: A total of 82 glioma patients were enrolled to our study. Pre-operative conventional MRI images (non-contrast T1/T2/flair/contrast-enhanced T1) and advanced MRI images (DAG and ADC mapping, MRI spectroscopy and perfusion MRI [PMRI]) were analyzed.
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