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The demineralized bone matrix (DBM) as the bone graft material to increase the fusion rate was widely used in spinal fusion. The current study aimed to compare the fusion rate of DBM to the fusion rate of autograft in lumbar spine fusion via meta-analysis of published literature. After systematic search, comparative studies were selected according to eligibility criteria. Checklist (risk of bias assessment tool for non-randomized study) was used to evaluate the risk of bias of the included nonrandomized controlled studies. The corresponding 95% confidence interval (95% CI) were calculated. We also used subgroup analysis to analyze the fusion rate of posterolateral lumbar fusion and lumbar interbody fusion. Eight studies were finally included in this meta-analysis. These eight studies included 581 patients. Among them, 337 patients underwent spinal fusion surgery using DBM (DBM group) and 204 patients underwent spinal fusion surgery with mainly autologous bone and without using DBM (control group). There was no significant differences of fusion rate between the two groups in posterolateral fusion analysis (risk ratio [RR], 1.03; 95% CI, 0.90-1.17; p=0.66) and interbody fusion analysis (RR, 1.13; 95% CI, 0.91-1.39; p=0.27). Based on the available evidence, the use of DBM with autograft in posterolateral lumbar spine fusion and lumbar interbody fusion showed a slightly higher fusion rate than that of autograft alone; however, there was no statistically different between two groups.
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http://dx.doi.org/10.3340/jkns.2019.0185 | DOI Listing |
Med Eng Phys
October 2025
College of Basic Medical Science, Shanxi University of Chinese Medicine, Jinzhong, 030619, Shanxi, China.
Pulse diagnosis holds a pivotal role in traditional Chinese medicine (TCM) diagnostics, with pulse characteristics serving as one of the critical bases for its assessment. Accurate classification of these pulse pattern is paramount for the objectification of TCM. This study proposes an enhanced SMOTE approach to achieve data augmentation, followed by multi-domain feature extraction.
View Article and Find Full Text PDFSpine Deform
September 2025
Spine Unit, Department of Orthopedic Surgery, Rigshospitalet, Inge Lehmanns Vej 6, 2100, Copenhagen, Denmark.
Study Design: This is a retrospective single-center study.
Purpose: The purpose is to investigate the incidence of distal junctional kyphosis (DJK) when fused proximal to the stable sagittal vertebra (SSV) in adolescent idiopathic scoliosis (AIS) patients undergoing selective thoracic fusion.
Methods: We retrospectively reviewed a consecutive cohort of surgically treated AIS patients with Lenke 1-2 A/B curves between 2011 and 2022 with a minimum of 2 years of follow-up.
Foot Ankle Int
September 2025
Harborview Medical Center, University of Washington, Department of Orthopaedics and Sports Medicine, Seattle, WA, USA.
Background: Talus fractures are rare injuries. To date, there is limited literature on outcomes after modern operative treatment of talus fractures. Many prior studies are limited by a small number of patients, limited follow-up, and include radiographic outcomes only.
View Article and Find Full Text PDFSkeletal Radiol
September 2025
Department of Orthopaedic Surgery, Northwestern University, Chicago, IL, USA.
Objective: To assess the ability of large language models (LLMs) to accurately simplify lumbar spine magnetic resonance imaging (MRI) reports.
Materials And Methods: Patients who underwent lumbar decompression and/or fusion surgery in 2022 at one tertiary academic medical center were queried using appropriate CPT codes. We then identified all patients with a preoperative ICD diagnosis of lumbar spondylolisthesis and extracted the latest preoperative spine MRI radiology report text.
Health Inf Sci Syst
December 2025
School of Information Science and Automation, Northeastern University, Shenyang, 110819 China.
Accurate prediction of drug-target interactions (DTIs) is crucial for improving the efficiency and success rate of drug development. Despite recent advancements, existing methods often fail to leverage interaction features at multiple granular levels, resulting in suboptimal data utilization and limited predictive performance. To address these challenges, we propose CF-DTI, a coarse-to-fine drug-target interaction model that integrates both coarse-grained and fine-grained features to enhance predictive accuracy.
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