This study evaluated the application of a mixture model involving a Weibull distribution function to predict the median times for restorations of three dental restorative materials to achieve unsatisfactory rating scores for six clinical factors. The accuracy of the method was assessed graphically against the known actuarial long-term deterioration observations of 1813 amalgam, 1774 anterior resin composite and 474 glass polyalkenoate (ionomer) cement restorations, assessed over periods of up to 20, 18 and 14 years, respectively. Of the six clinical factors investigated (which included marginal fracture), only four had sufficient long-term unsatisfactory rating score data to enable their median times to be predicted. These predicted times were: for amalgam restorations, surface roughness 32.5 years and surface tarnishing 16.0 years; for resin composites, marginal staining 25.4 years and colour mismatch 14.2 years; and for glass polyalkenoate (ionomer) cements, marginal staining 17.6 years and colour mismatch 3.6 years. The known and predictive unsatisfactory rating score results were generally in close agreement. However, it was not possible to predict median times for unsatisfactory rating scores associated with very slowly deteriorating restoration factors. The actual replacement rates of the amalgam restorations were too low to obtain their median survival time. However, for the faster failing resin composites this time was 7.9 +/- 0.5 years, and for the glass polyalkenoate (ionomer) cements 2.2 +/- 0.2 years. The relationship of restoration deterioration to restoration replacement and dental health requires further analysis.
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http://dx.doi.org/10.1016/0300-5712(92)90082-n | DOI Listing |
J Gen Intern Med
January 2025
Department of Primary Care and Emergency Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan.
Purpose: Our purpose was to evaluate the characteristics of highly and poorly rated teachers as well as to assess the validity and reliability of those evaluations.
Methods: We downloaded 14 years of medicine faculty evaluations completed by 3rd and 4 year medical students. We dichotomized overall teaching effectiveness as outstanding (receiving "outstanding") or inferior (rated as a "unsatisfactory," "marginal," or "acceptable").
Clin J Sport Med
December 2024
Department of Orthopaedic Surgery, University Hospitals - Drusinsky Sports Medicine Institute, Case Western Reserve University, Cleveland, Ohio.
Objective: This study aims to analyze the ability of ChatGPT to answer frequently asked questions (FAQs) regarding FAI. We hypothesize that ChatGPT can provide accurate and thorough responses when presented with FAQs regarding FAI.
Design: Ten FAQs regarding FAI were presented to ChatGPT 3.
J ISAKOS
December 2024
Division of Orthopaedic Surgery, Department of Surgery, McMaster University, 1200 Main St West, Hamilton, Ontario, L8N 3Z5, Canada. Electronic address:
Objectives: This study aimed to evaluate the accuracy of ChatGPT in answering patient questions about femoroacetabular impingement (FAI) and arthroscopic hip surgery, comparing the performance of versions ChatGPT-3.5 (free) and ChatGPT-4 (paid).
Methods: Twelve frequently asked questions (FAQs) relating to FAI were selected and posed to ChatGPT-3.
Brain Behav
November 2024
Jockey Club School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong.
Background: Meta-analyses (MAs) provide up-to-date, quantified evidence on treatment effects, which may be useful for clinical and policy decision-making. However, the quality of MAs varies, and methodological flaws can limit their reliability.
Aims: This review evaluated the methodological quality of MAs on sleep disorder treatments.
BMC Health Serv Res
November 2024
Center for World Health Organization Studies, School of Health Management, Southern Medical University, Guangzhou, China.
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