Department of Restorative Dentistry, Nigde Ömer Halisdemir University, Niğde, Turkey.
Department of Endodontics and Restorative Dentistry, School of Dental Medicine, University of Zagreb, Gunduliceva 5, Zagreb, Croatia.
J Dent. 2024 Oct;149:105289. doi: 10.1016/j.jdent.2024.105289. Epub 2024 Aug 3.
To systematically compile data on the degree of conversion (DC) for bulk-fill composites using a network meta-analysis.
A systematic search for in vitro studies of DC of bulk-fill composites was performed in PubMed, Web of Science, Scopus, and Open Grey. Risk of bias within studies and due to missing evidence was assessed using the Joanna Briggs Institute scoring system and ROB-MEN tool, respectively. The primary outcome was the DC of bulk-fill composites. Surface Under the Cumulative Ranking curve (SUCRA) was used to rank relative performance. Inconsistencies in the model were investigated to ensure its validity and the level of confidence in the network meta-analysis (CINeMA) was assessed.
A total of 28 studies were included in the quantitative analysis. The average DC values (%) for 0-h/top, 0-h/bottom, 24-h/top, and 24-h/bottom were 59.09, 57.14, 66.73, and 63.87, respectively. According to their SUCRA ranking, the best-performing composites were: SonicFill, Venus Bulk Fill, and SDR (0-h/top), Reveal HD, i-Flow Bulk Fill, and Venus Bulk- Fill (0-h/bottom), Venus Bulk Fill, SDR, and QuiXfil (24-h/top), and Venus Bulk Fill, Aura Bulk Fill, and i-Flow Bulk Fill (24-h/bottom). Incoherence between direct and indirect evidence was identified as the most significant factor affecting confidence.
DC values of bulk-fill composites were within the range commonly reported for previous generations of "conventional" composites, with flowable composites tending to perform better than sculptable composites. High variability in DC data was observed, which may be attributed to incompletely understood methodological differences.
DC is a fundamental parameter that influences multiple mechanical and biological properties of resin composites and is particularly relevant for the group of bulk-fill composites that are designed for use in thick layers.
通过网络荟萃分析系统地编译有关大体积充填复合材料转化率（DC）的数据。
在 PubMed、Web of Science、Scopus 和 Open Grey 中进行了关于大体积充填复合材料 DC 的体外研究的系统检索。使用 Joanna Briggs 研究所评分系统和 ROB-MEN 工具评估研究内和由于证据缺失引起的偏倚风险。主要结局为大体积充填复合材料的 DC。累积排序曲线下面积（SUCRA）用于对相对性能进行排名。为确保模型的有效性和网络荟萃分析的置信水平（CINeMA），调查了模型中的不一致性。
共有 28 项研究纳入定量分析。0-h/top、0-h/bottom、24-h/top 和 24-h/bottom 的平均 DC 值（%）分别为 59.09、57.14、66.73 和 63.87。根据它们的 SUCRA 排名，表现最好的复合材料是：SonicFill、Venus Bulk Fill 和 SDR（0-h/top）、Reveal HD、i-Flow Bulk Fill 和 Venus Bulk-Fill（0-h/bottom）、Venus Bulk Fill、SDR 和 QuiXfil（24-h/top）以及 Venus Bulk Fill、Aura Bulk Fill 和 i-Flow Bulk Fill（24-h/bottom）。直接证据和间接证据之间的不一致被认为是影响置信度的最重要因素。
大体积充填复合材料的 DC 值在之前几代“常规”复合材料中报告的范围内，可流动性复合材料的性能优于可雕刻性复合材料。观察到 DC 数据的高度变异性，这可能归因于对不完全理解的方法学差异。
DC 是影响树脂复合材料多种机械和生物学性能的基本参数，对于旨在用于厚层的大体积充填复合材料尤其相关。
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