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Musculoskeletal simulation of elbow stability for common injury patterns
Get PDFElbow stability is derived from a combination of muscular, ligamentous, and bony structures. After an elbow trauma the stability of the joint is an important decision criterion for the subsequent treatment. The decision regarding nonoperative/operative care depends mostly on subjective assessments of medical experts. Therefore, the aim of this study is to use musculoskeletal simulations as an objective assessment tool to investigate the extent to which failure of different stabilizers affects the elbow stability and how these observations correspond to the assessment from clinical practice. A musculoskeletal elbow simulation model was developed for this aim. To investigate the stability of the elbow, varus/valgus moments were applied under 0°, 45°, and 90° flexion while the respective cubital angle was analyzed. This was performed for nine different injury scenarios, which were also evaluated for stability by clinical experts. With the results, it can be determined by which injury pattern and under which flexion angle the elbow stability is impaired regarding varus/valgus moments. The scenario with a complete failure of the medial and lateral ligaments and a fracture of the radial head was identified as having the greatest instability. The study presented a numerical determination of elbow stability against varus/valgus moments regarding clinical injury patterns, as well as a comparison of the numerical outcome with experience gained in clinical practice. The numerical predictions agree well with the assessments of the clinical specialists. Thus, the results from musculoskeletal simulation can make an important contribution to a more objective assessment of the elbow stability
Benchmarking R&D success rates of leading pharmaceutical companies: an empirical analysis of FDA approvals (2006–2022)
Get PDFPrevious analyses provide an industry benchmark of ∼10% for the success rate in clinical development. However, prior analyses were limited by a narrow timeframe, a diverse research focus, biases in phase-to-phase transition methodology or a focus on specific use cases. We calculated unbiased input:output ratios (Phase I to FDA new drug approval) to analyze the likelihood of first approval using data from clinicaltrials.gov, encompassing a total of 2092 active ingredients, 19 927 clinical trials conducted by 18 leading pharmaceutical companies (2006–2022) and 274 new drug approvals. Our study reveals an average likelihood of first approval rate of 14.3% across leading research-based pharmaceutical companies, broadly ranging from 8% to 23%
Packet Rate Control for Maneuver Coordination in Congested V2X Communication Environments
No full textKonzeption und prototypische Entwicklung eines Brettspiels zur interaktiven Gestaltung der Lehrveranstaltung „Wissensmanagement“ mittels Gamification
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Document GraphRAG: Knowledge Graph Enhanced Retrieval Augmented Generation for Document Question Answering Within the Manufacturing Domain
Get PDFRetrieval-Augmented Generation (RAG) systems have shown significant potential for domain-specific Question Answering (QA) tasks, although persistent challenges in retrieval precision and context selection continue to hinder their effectiveness. This study introduces Document Graph RAG (GraphRAG), a novel framework that bolsters retrieval robustness and enhances answer generation by incorporating Knowledge Graphs (KGs) built upon a document’s intrinsic structure into the RAG pipeline. Through the application of the Design Science Research methodology, we systematically design, implement, and evaluate GraphRAG, leveraging graph-based document structuring and a keyword-based semantic linking mechanism to improve retrieval quality. The evaluation, conducted on well-established datasets including SQuAD, HotpotQA, and a newly developed manufacturing dataset, demonstrates consistent performance gains over a naive RAG baseline across both retrieval and generation metrics. The results indicate that GraphRAG improves Context Relevance metrics, with task-dependent optimizations for chunk size, keyword density, and top-k retrieval further enhancing performance. Notably, multi-hop questions benefit most from GraphRAG’s structured retrieval strategy, highlighting its advantages in complex reasoning tasks