Meta-analytical Review of Parameters Involved in Dentin Bonding
Метааналитический обзор параметров, влияющих на адгезию к дентину
2011-12-14
SCID: 54.1/u7mgudug
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all-in-one adhesivesbond-strength testingdentin bondingmicro-tensile testneural network statistical model
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Abstract (AI)
Bond-strength testing is the method most used for the assessment of bonding effectiveness to enamel and dentin. We aimed to disclose general trends in adhesive performance by collecting dentin bond-strength data systematically. The PubMed and EMBASE databases were used to identify 2,157 bond-strength tests in 298 papers. Most used was the micro-tensile test, which appeared to have a larger discriminative power than the traditional macro-shear test. Because of the huge variability in dentin bond-strength data and the high number of co-variables, a neural network statistical model was constructed. Variables like 'research group' and 'adhesive brand' appeared most determining. Weighted means derived from this analysis confirmed the high sensitivity of current adhesive approaches (especially of all-in-one adhesives) to long-term water-storage and substrate variability.
Key Findings
1
A neural-network model was developed to account for substantial variability in dentin bond-strength data and numerous covariates.
2
A systematic review identified 2,157 dentin bond-strength tests across 298 papers using PubMed and EMBASE.
3
Micro-tensile testing was the most commonly used method and appeared more discriminative than traditional macro-shear testing.
4
Research group and adhesive brand were among the strongest determinants of reported dentin bonding performance.
5
Weighted analyses showed that current adhesive systems, particularly all-in-one adhesives, are highly sensitive to long-term water storage and substrate variability.
Research Object
dentin bonding and dental adhesive systems
Research Subject
general trends and determinants of bond-strength performance, including the effects of testing method, adhesive brand, long-term water storage, and substrate variability
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2011-12-14
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