Objectives To identify patient‐reported experience measures (PREMs), assess their validity and reliability, and assess any bias in the study design of PREM validity and reliability testing. Data Sources/Study Setting Articles reporting on PREM development and testing sourced from MEDLINE, CINAHL and Scopus databases up to March 13, 2018. Study Design Systematic review. Data Collection/Extraction Methods Critical appraisal of PREM study design was undertaken using the Appraisal tool for Cross‐Sectional Studies (AXIS). Critical appraisal of PREM validity and reliability was undertaken using a revised version of the COSMIN checklist. Principal Findings Eighty‐eight PREMs were identified, spanning across four main health care contexts. PREM validity and reliability was supported by appropriate study designs. Internal consistency (n = 58, 65.2 percent), structural validity (n = 49, 55.1 percent), and content validity (n = 34, 38.2 percent) were the most frequently reported validity and reliability tests. Conclusions Careful consideration should be given when selecting PREMs, particularly as seven of the 10 validity and reliability criteria were not undertaken in ≥50 percent of the PREMs. Testing PREM responsiveness should be prioritized for the application of PREMs where the end user is measuring change over time. Assessing measurement error/agreement of PREMs is important to understand the clinical relevancy of PREM scores used in a health care evaluation capacity.

The results suggest that tacrolimus 0.1% cream is an effective alternative to topical steroid and can be considered a first-line therapy in OLP. However, further studies are needed to confirm the effectiveness of this treatment before it is recommended for use in clinical practice.

The use of CUAs in evaluation of oral health interventions has been increasing recently, especially from 2011 to 2016. The majority of CUA articles were of good reporting quality as assessed by the CHEERS checklist and were able to provide conclusions regarding the most cost-effective intervention among the different options compared: this will assist in healthcare decision-making and resource allocation. These positive outcomes of our study encourage wider use of CUAs within the dental and oral health professions.

To identify the generic or disease-specific pediatric quality of life (QoL) instruments used in oral health research among children and adolescents and to provide an overview of these QoL instruments. Methods: A systematic literature search was performed with multiple databases to identify the pediatric QoL instruments used in oral health research. Results: The literature search yielded 872 records; from these, 16 pediatric QoL instruments were identified that had been used among children and adolescents in oral health research. Of these, 11 were oral healthespecific QoL instruments and five were generic instruments. Of the 11 oral health especific QoL instruments, none were multiattribute utility instruments (MAUI), whereas of the five generic instruments, two (Child Health Utility 9D index and EuroQoL-5D youth) were classified as an MAUI. Except for one, all pediatric QoL instruments were published after the year 2000 and the majority originated from the USA (n ¼ 8). Of the 11 oral healthespecific QoL instruments, five instruments are designed for the respondent to be a child (i.e., selfreport), one uses proxy responses from a parent or guardian, and five instruments have both self and proxy versions. Of the five generic QoL instruments, one uses proxy responses and the other four instruments have both self and proxy versions. Conclusions: This review identified a wide variety of pediatric oral health especific and generic QoL instruments used in oral health research among children and adolescents. The availability of these QoL instruments provides researchers with the opportunity to select the instrument most suited to address their research question.