Introduction: Proper management of thoracic drainages is essential in the recovery of patients after lung resection. This study evaluates the concordance in decision-making for drain removal depending on the type of drainage system used and the previous experience of the personnel.
Material And Methods: Prospective, comparative, and stratified randomization study on interobserver variability between senior specialist doctors and inexperienced healthcare personnel in the removal of thoracic drains in patients undergoing lung resection connected to conventional systems (CS) or digital systems (DS) with continuous recording. The withdrawal criteria were established before the study, and decisions were recorded during three postoperative days.
Results: 75 patients were included, 38 CS and 37 DS, with no statistically significant differences in sex distribution, age, intervention performed, presence of pleuropulmonary adhesions, drain time, or post-extraction complications between the groups. The overall concordance in drain removal decisions was moderate (kappa = 0.452), with notable variations in concordance depending on the drainage system used: CS (kappa = 0.188) with an overall agreement rate of 61.7% compared to DS (kappa = 0.716) with an overall agreement rate of 86.4%. Digital systems showed substantial concordance regardless of the operator's experience, with kappa values indicating high concordance on all postoperative days.
Conclusions: The use of digital systems for managing thoracic drains significantly improves concordance in clinical decision-making regardless of the experience level. These findings suggest that adopting digital systems not only optimizes patient safety but also reduces the dependence on highly specialized healthcare professionals.
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http://dx.doi.org/10.1016/j.cireng.2024.09.013 | DOI Listing |
Prostate Int
September 2024
Gazi University School of Medicine, Urology Department, Ankara, Turkey.
Aim: To investigate the predictive value of lesion length in multiparametric prostate magnetic resonance imaging with respect to prostate volume for clinically significant prostate cancer diagnosis in targeted biopsies.
Materials And Methods: The data of biopsy-naïve patients in the Turkish Urooncology Association Prostate Cancer Database who underwent targeted prostate biopsies were included in this study. Lesion density is calculated as the ratio of lesion length (mm) in MR to prostate volume (cc).
Healthc Technol Lett
January 2025
This study aimed to develop an advanced ensemble approach for automated classification of mental health disorders in social media posts. The research question was: can an ensemble of fine-tuned transformer models (XLNet, RoBERTa, and ELECTRA) with Bayesian hyperparameter optimization improve the accuracy of mental health disorder classification in social media text. Three transformer models (XLNet, RoBERTa, and ELECTRA) were fine-tuned on a dataset of social media posts labelled with 15 distinct mental health disorders.
View Article and Find Full Text PDFHeliyon
July 2024
College of Engineering and IT, University of Dubai, Academic City, 14143, Dubai, United Arab Emirates.
This study proposes a hierarchical automated methodology for detecting brain tumors in Magnetic Resonance Imaging (MRI), focusing on preprocessing images to improve quality and eliminate artifacts or noise. A modified Extreme Learning Machine is then used to diagnose brain tumors that are integrated with the Modified Sailfish optimizer to enhance its performance. The Modified Sailfish optimizer is a metaheuristic algorithm known for efficiently navigating optimization landscapes and enhancing convergence speed.
View Article and Find Full Text PDFHeliyon
July 2024
Deakin Health Economics, Deakin University, Melbourne, Victoria, Australia.
Current research into the digital healthcare landscape reveals a significant gap in understanding the perspectives of consumers with lived health experiences on sharing their health data for research purposes. Despite the substantial value that such shared information can bring to healthcare research, policy development, and system improvement, insights into the attitudes and willingness of these consumers towards data sharing remain sparse. This study seeks to fill this gap, exploring the unique views of these individuals and assessing the potential benefits their data sharing could contribute to healthcare.
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