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Objective: We aimed to identify published classification systems with a targeted treatment approach (treatment-based classification systems (TBCSs)) for patients with non-specific neck pain, and assess their quality and effectiveness.
Design: Systematic review.
Data Sources: MEDLINE, CINAHL, EMBASE, PEDro and the grey literature were systematically searched from inception to December 2019.
Study Appraisal And Synthesis: The main selection criterium was a TBCS for patients with non-specific neck pain with physiotherapeutic interventions. For data extraction of descriptive data and quality assessment we used the framework developed by Buchbinder et al. We considered as score of ≤3 as low quality, a score between 3 and 5 as moderate quality and a score ≥5 as good quality. To assess the risk of bias of studies concerning the effectiveness of TBCSs (only randomized clinical trials (RCTs) were included) we used the PEDro scale. We considered a score of ≥ six points on this scale as low risk of bias.
Results: Out of 7664 initial references we included 13 studies. The overall quality of the TBCSs ranged from low to moderate. We found two RCTs, both with low risk of bias, evaluating the effectiveness of two TBCSs compared to alternative treatments. The results showed that both TBCSs were not superior to alternative treatments.
Conclusion: Existing TBCSs are, at best, of moderate quality. In addition, TBCSs were not shown to be more effective than alternatives. Therefore using these TBCSs in daily practice is not recommended.
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http://dx.doi.org/10.1016/j.msksp.2020.102133 | DOI Listing |
Ann Ital Chir
December 2024
Department of General Surgery, Faculty of Medicine, Dicle University, 21280 Diyarbakır, Türkiye.
Aim: In early-stage breast cancer, the axillary lymph nodes play a crucial role in determining the prognosis of the disease. The rate of lymph node involvement might be a more valuable prognostic factor than the number of positive lymph nodes. Therefore, we aimed to evaluate whether the lymph node ratio (LNR) is a superior prognostic indicator compared to the pathologic lymph node count in early-stage disease.
View Article and Find Full Text PDFAnn Ital Chir
December 2024
Department of Colorectal Surgery, Hubei Provincial Hospital of Traditional Chinese Medicine Affiliated to Hubei University of Chinese Medicine, 430071 Wuhan, Hubei, China.
Aim: Anorectal diseases, often requiring surgical intervention and careful post-operative wound management, pose substantial challenges in healthcare. This study presents a novel application of artificial intelligence, specifically machine learning, aimed at improving the classification and analysis of post-surgical wound images. By doing so, it seeks to enhance patient outcomes through personalized and optimized wound care strategies.
View Article and Find Full Text PDFAnn Ital Chir
December 2024
Multidisciplinary Department of Medical-Surgical and Dental Specialties, Plastic Surgery Unit, Università degli Studi della Campania "Luigi Vanvitelli", 80138 Naples, Italy.
Aim: Varicose veins, while frequently perceived as a cosmetic concern, should be evaluated within the broader context of chronic venous insufficiency (CVI) affecting the lower extremities. The Mappa Emodinamica Venosa Condivisa, Italian for Shared Venous Hemodynamics Map (MEVeC) project aims to provide an objective method for assessing the hemodynamic disorders underlying CVI and standardizing the evaluation of therapeutic interventions for lower extremity CVI. To achieve objective treatment assessment, it is essential to ensure uniform expertise among phlebologists and the ability to replicate comparable clinical outcomes.
View Article and Find Full Text PDFJ Biopharm Stat
December 2024
Department of Biostatistics, University of Veterinary Medicine Budapest, Budapest, Hungary.
In recent years, an increasing number of publications on the analysis of binary data have applied methods that take misclassification into account. However, potential misclassification is often ignored in study design due to the lack of sample size formulas or software. This may lead to a considerable loss of power in studies that only account for misclassification at the analysis stage.
View Article and Find Full Text PDFFront Neurosci
December 2024
Institute of Cell Biology and Neurobiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin und Humboldt-Universität zu Berlin, Berlin, Germany.
Introduction: Cohen syndrome (CS) is an early-onset pediatric neurodevelopmental disorder characterized by postnatal microcephaly and intellectual disability. An accurate diagnosis for individuals with CS is crucial, particularly for their caretakers and future prospects. CS is predominantly caused by rare homozygous or compound heterozygous pathogenic variants in the vacuolar protein sorting-associated 13B () gene, which disrupt protein translation and lead to a loss of function (LoF) of the encoded VPS13B protein.
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