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Longevity of Polymer-Infiltrated Ceramic Network and Zirconia-Reinforced Lithium Silicate Restorations: A Systematic Review and Meta-Analysis.

William BanhJared HughesAaron SiaDavid C H ChienSantosh K TadakamadlaCarlos Marcelo da Silva FigueredoKhaled Elsayed Ahmed
Published in: Materials (Basel, Switzerland) (2021)
The purpose of this study was to systematically review the existing literature to assess the clinical survival and success of PICN and ZLS indirect restorations as the clinical evidence for them remains lacking. PubMed, SCOPUS, Embase, Cochrane Library, Web of Science, LILACs, and SciElo databases were searched from 1 January 2000 to 1 February 2021. Clinical trials and cohort studies published in English were included while case studies, case series, and in vitro studies were excluded. Results were analyzed qualitatively and a meta-analysis using a random effects model was performed. A strength of recommendation taxonomy (SORT) analysis was conducted and risk of bias (RoB) was assessed using the Newcastle-Ottawa scale and Cochrane RoB tools. An electronic search through the databases yielded 2454 articles, of which 825 remained after duplicate removal. Five studies investigating PICN and four investigating ZLS indirect restorations remained after assessing for eligibility. The overall survival rate of PICN over 1 year was 99.6% and 99.2% over 2 years. The overall survival rate of ZLS over 1 year was 99%. The main mode of failure for both materials was catastrophic fracture. One study had a high RoB, four had a moderate RoB, and four had a low RoB. Both materials demonstrated moderate strength of recommendation at a level 2 evidence for all studies based on SORT analysis. PICN and ZLS show promising short-term clinical performance as full and partial coverage indirect restorations, but longer follow-up studies are required to confirm their long-term performance.
Keyphrases
  • clinical trial
  • case control
  • systematic review
  • free survival
  • healthcare
  • machine learning
  • big data
  • deep learning
  • data analysis