Purpose: The objective of this meta-analysis is to compare available computer-assisted navigation platforms by key performance metrics including pedicle screw placement accuracy, operative time, neurological complications, and blood loss.
Methods: A systematic review was conducted using major databases for articles comparing pedicle screw accuracy of computer-assisted navigation to conventional (freehand or fluoroscopy) controls via post-operative computed tomography. Outcome data were extracted and pooled by random-effects model for analysis.
Results: All navigation platforms demonstrated significant reduction in risk of breach, with Stryker demonstrating the highest accuracy compared to controls (OR 0.16 95% CI 0.06 to 0.41, P < 0.00001, I = 0%) followed by Medtronic. There were no significant differences in accuracy or most surgical outcome measures between platforms; however, BrainLab demonstrated significantly faster operative time compared to Medtronic by 30 min (95% CI - 63.27 to - 2.47, P = 0.03, I = 74%). Together, there was significantly lower risk of major breach in the navigation group compared to controls (OR 0.42, 95% CI 0.27-0.63, P < 0.0001, I = 56%).
Conclusions: When comparing between platforms, Stryker demonstrated the highest accuracy, and Brainlab the shortest operative time, both followed by Medtronic. No significant difference was found between platforms regarding neurologic complications or blood loss. Overall, our results demonstrated a 60% reduction in risk of major breach utilizing computer-assisted navigation, coinciding with previous studies, and supporting its validity. This study is the first to directly compare available navigation platforms offering insight for further investigation and aiding in the institutional procurement of platforms. LEVEL 3 EVIDENCE: Meta-analysis of Level 3 studies.
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http://dx.doi.org/10.1007/s00586-023-07865-4 | DOI Listing |
JMIR Hum Factors
January 2025
Institute of General Practice, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany.
Background: The internet is a key source of health information, but the quality of content from popular search engines varies, posing challenges for users-especially those with low health or digital health literacy. To address this, the "tala-med" search engine was developed in 2020 to provide access to high-quality, evidence-based content. It prioritizes German health websites based on trustworthiness, recency, user-friendliness, and comprehensibility, offering category-based filters while ensuring privacy by avoiding data collection and advertisements.
View Article and Find Full Text PDFAfr J Prim Health Care Fam Med
December 2024
Division of Rural Health (Ukwanda), Department of Global Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa; and, Department of Health Professions Education, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town.
Background: Interprofessional education (IPE) during undergraduate training (UGT) is considered important for new graduates to collaborate inter-professionally. There are, however, well-documented workplace challenges that hinder their involvement in interprofessional collaborative practice (IPCP) such as professional hierarchy, poor role clarification and communication challenges.
Aim: This article explores graduates' perceptions of the value rural undergraduate IPE had on their IPCP during their first year of work.
Digit Health
January 2025
Department of Occupational Science and Occupational Therapy, University of British Columbia, Vancouver, BC, Canada.
Background: TikTok is a global social media platform with over 1 billion active users. Presently, there are few data on how TikTok users navigate the platform for mental health purposes and the content they view.
Objective: This study aims to understand the patterns of mental health-related content on TikTok and assesses the accuracy and quality of the advice and information provided.
Front Big Data
January 2025
AI Institute, University of South Carolina, Columbia, SC, United States.
The emergence of advanced artificial intelligence (AI) models has driven the development of frameworks and approaches that focus on automating model training and hyperparameter tuning of end-to-end AI pipelines. However, other crucial stages of these pipelines such as dataset selection, feature engineering, and model optimization for deployment have received less attention. Improving efficiency of end-to-end AI pipelines requires metadata of past executions of AI pipelines and all their stages.
View Article and Find Full Text PDFInt J Spine Surg
January 2025
Service de chirurgie orthopédique et traumatologique, Université Grenoble Alpes, center hospitalier universitaire de Grenoble, La Tronche, France.
Background: Surgeons' reliance on intraoperative fluoroscopy during vertebroplasty procedures has raised concerns regarding the level of patient and surgeon radiation. Navigation systems have shown a potential to reduce the overall patient and medical staff exposure during dose exposure studies. The main objective of this study was to determine whether the Surgivisio platform (eCential Robotics, France), a unified imaging and navigation platform, lowers the patient dose during routine clinical usage as compared with published fluoroscopy and other navigation options that are published in the literature.
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