Methodological bias can directly affect the interpretation of research data. Studies reporting on excavated skeletons represent a valuable source of information in medicine, dentistry, archaeology... Show moreMethodological bias can directly affect the interpretation of research data. Studies reporting on excavated skeletons represent a valuable source of information in medicine, dentistry, archaeology and anthropology, and forensic sciences. However, these studies represent a specific setting with their own methodology, for which no quality assessment tool is available. The aim was to develop a critical appraisal tool to assess the methodological quality of studies reporting on archaeologically excavated human skeletons. An international Delphi study was therefore conducted to support item generation and ensure content validity for a new tool. Experts from the following domains were consulted: dentistry, forensic sciences, archaeology and anthropology, general medicine, epidemiology, and statistics. Participants judged the relevance and comprehensiveness of items retrieved from the literature. Consensus was predefined as 75% agreement between experts, and achieved within two Delphi rounds. As a result, 44 and 32 participants completed the first and second Delphi rounds, respectively, achieving consensus on 17 items. This research provides the first evidence-based tool for the methodological assessment of studies reporting on archaeologically excavated skeletons. Clinicians and researchers can use this tool for critical appraisal of studies or when performing systematic reviews. Future research will focus on psychometric testing of the newly developed tool. Show less
Background: The impact of precision psychiatry for clinical practice has not been systematically appraised. This study aims to provide a comprehensive review of validated prediction models to... Show moreBackground: The impact of precision psychiatry for clinical practice has not been systematically appraised. This study aims to provide a comprehensive review of validated prediction models to estimate the individual risk of being affected with a condition (diagnostic), developing outcomes (prognostic), or responding to treatments (predictive) in mental disorders. Methods: PRISMA/RIGHT/ CHARMS-compliant systematic review of the Web of Science, Cochrane Central Register of Reviews, and Ovid/PsycINFO databases from inception until July 21, 2019 (PROSPERO CRD42019155713) to identify diagnostic/prognostic/predictive prediction studies that reported individualized estimates in psychiatry and that were internally or externally validated or implemented. Random effect meta-regression analyses addressed the impact of several factors on the accuracy of prediction models. Findings: Literature search identified 584 prediction modeling studies, of which 89 were included. 10.4% of the total studies included prediction models internally validated (n = 61), 4.6% models externally validated (n = 27), and 0.2% (n = 1) models considered for implementation. Across validated prediction modeling studies (n = 88), 18.2% were diagnostic, 68.2% prognostic, and 13.6% predictive. The most frequently investigated condition was psychosis (36.4%), and the most frequently employed predictors clinical (69.5%). Unimodal compared to multimodal models (beta = .29, P = .03) and diagnostic compared to prognostic (beta = .84, p < .0001) and predictive (beta = .87, P = .002) models were associated with increased accuracy. Interpretation: To date, several validated prediction models arc available to support the diagnosis and prognosis of psychiatric conditions, in particular, psychosis, or to predict treatment response. Advancements of knowledge are limited by the lack of implementation research in real-world clinical practice. A new generation of implementation research is required to address this translational gap. Show less
