Introduction: Nonoperative care represents a cornerstone of adolescent idiopathic scoliosis (AIS) management, although no consensus exists for a minimal data set. We aimed to determine a consensus in critical data points to obtain during clinical AIS visits.
Methods: A REDCap-based survey was distributed to Pediatric Orthopedic Society of America (POSNA), Pediatric Spine Study Group (PSSG), and International Society on Scoliosis Orthopedic and Rehabilitation Treatment (SOSORT). Respondents ranked the importance of data points in history, physical examination, and bracing during AIS visits. Results: One hundred eighty-one responses were received (26% response rate), of which 86% were physicians and 14% were allied health professionals. About 80% of respondents worked at pediatric hospitals or pediatric spaces within adult hospitals, and 82% were academic, with the majority (57%) seeing 150+ unique AIS patients annually. Most respondents recommended six-month follow-up for patients under observation (60%) and bracing (54%). Most respondents (75%) considered family history and pain important (69%), with the majority (69%) asking about pain at every visit. Across all time points, Adam's forward bend test, shoulder level, sagittal contour, trunk shift, and curve stiffness were all considered critically important (>60%). At the first visit, scapular prominence, leg lengths, motor and neurological examination, gait, and iliac crest height were also viewed as critical. At the preoperative visit, motor strength and scapular prominence should also be documented. About 39% of respondents use heat sensors to monitor bracing compliance, and average brace wear since the prior visit was considered the most important (85%) compliance data point.
Conclusions: This study establishes recommendations for a 19-item minimum data set for clinical AIS evaluation, including history, physical exam, and bracing, to allow for future multicenter registry-based studies.
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http://dx.doi.org/10.7759/cureus.58332 | DOI Listing |
BMC Public Health
January 2025
Al-Barkaat Institute of Management Studies, Aligarh 202122, Dr. A. P. J. Abdul Kalam Technical University, Lucknow 226010, India.
Cardiovascular disease (CVD) is a leading cause of death and disability worldwide, and its incidence and prevalence are increasing in many countries. Modeling of CVD plays a crucial role in understanding the trend of CVD death cases, evaluating the effectiveness of interventions, and predicting future disease trends. This study aims to investigate the modeling and forecasting of CVD mortality, specifically in the Sindh province of Pakistan.
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December 2024
Guangdong Provincial Key Laboratory of Pharmaceutical Bioactive Substances, Guangdong Pharmaceutical University, Guangzhou 510006, PR China. Electronic address:
In the present study, we uncovered and validated potential biomarkers related to gout, characterized by the accumulation of sodium urate crystals in different joint and non-joint structures. The data set GSE160170 was obtained from the GEO database. We conducted differential gene expression analysis, GO enrichment assessment, and KEGG pathway analysis to understand the underlying processes.
View Article and Find Full Text PDFAnal Chem
January 2025
Separation Science Group, Department of Organic and Macromolecular Chemistry, Ghent University, Krijgslaan 281 S4bis, B-9000 Ghent, Belgium.
Addressing the global challenge of ensuring access to safe drinking water, especially in developing countries, demands cost-effective, eco-friendly, and readily available technologies. The persistence, toxicity, and bioaccumulation potential of organic pollutants arising from various human activities pose substantial hurdles. While high-performance liquid chromatography coupled with high-resolution mass spectrometry (HPLC-HRMS) is a widely utilized technique for identifying pollutants in water, the multitude of structures for a single elemental composition complicates structural identification.
View Article and Find Full Text PDFJ Am Chem Soc
January 2025
Institute of Materials for Electronics and Energy Technology (i-MEET), Department of Materials Science and Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Martensstraße 7, 91058 Erlangen, Germany.
Emerging photovoltaics for outer space applications are one of the many examples where radiation hard molecular semiconductors are essential. However, due to a lack of general design principles, their resilience against extra-terrestrial high-energy radiation can currently not be predicted. In this work, the discovery of radiation hard materials is accelerated by combining the strengths of high-throughput, lab automation and machine learning.
View Article and Find Full Text PDFJ Phys Chem A
January 2025
Liaoning Key Laboratory of Manufacturing System and Logistics Optimization, Shenyang 110819, China.
Artificial intelligence technology has introduced a new research paradigm into the fields of quantum chemistry and materials science, leading to numerous studies that utilize machine learning methods to predict molecular properties. We contend that an exemplary deep learning model should not only achieve high-precision predictions of molecular properties but also incorporate guidance from physical mechanisms. Here, we propose a framework for predicting molecular properties based on data-driven electron density images, referred to as D3-ImgNet.
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