We found insufficient evidence to support the use of routine outcome monitoring using PROMs in the treatment of CMHDs, in terms of improving patient outcomes or in improving management. The findings are subject to considerable uncertainty however, due to the high risk of bias in the large majority of trials meeting the inclusion criteria, which means further research is very likely to have an important impact on the estimate of effect and is likely to change the estimate. More research of better quality is therefore required, particularly in primary care where most CMHDs are treated.Future research should address issues of blinding of assessors and attrition, and measure a range of relevant symptom outcomes, as well as possible harmful effects of monitoring, health-related quality of life, social functioning, and costs. Studies should include people treated with drugs as well as psychological therapies, and should follow them up for longer than six months.

This article presents certain recent methodologies and some new results for the statistical analysis of probability distributions on manifolds. An important example considered in some detail here is the 2-D shape space of k-ads, comprising all configurations of $k$ planar landmarks ($k>2$)-modulo translation, scaling and rotation.Comment: Published in at http://dx.doi.org/10.1214/074921708000000200 the IMS Collections (http://www.imstat.org/publications/imscollections.htm) by the Institute of Mathematical Statistics (http://www.imstat.org

Artificial intelligence (AI) promises to change health care, with some studies showing proof of concept of a providerlevel performance in various medical specialties. However, there are many barriers to implementing AI, including patient acceptance and understanding of AI. Patients' attitudes toward AI are not well understood. We systematically reviewed the literature on patient and general public attitudes toward clinical AI (either hypothetical or realised), including quantitative, qualitative, and mixed methods original research articles. We searched biomedical and computational databases from Jan 1, 2000, to Sept 28, 2020, and screened 2590 articles, 23 of which met our inclusion criteria. Studies were heterogeneous regarding the study population, study design, and the field and type of AI under study. Six (26%) studies assessed currently available or soon-to-be available AI tools, whereas 17 (74%) assessed hypothetical or broadly defined AI. The quality of the methods of these studies was mixed, with a frequent issue of selection bias. Overall, patients and the general public conveyed positive attitudes toward AI but had many reservations and preferred human supervision. We summarise our findings in six themes: AI concept, AI acceptability, AI relationship with humans, AI development and implementation, AI strengths and benefits, and AI weaknesses and risks. We suggest guidance for future studies, with the goal of supporting the safe, equitable, and patient-centred implementation of clinical AI.

SUMMARYStatistical analysis on landmark-based shape spaces has diverse applications in morphometrics, medical diagnostics, machine vision and other areas. These shape spaces are non-Euclidean quotient manifolds. To conduct nonparametric inferences, one may define notions of centre and spread on this manifold and work with their estimates. However, it is useful to consider full likelihood-based methods, which allow nonparametric estimation of the probability density. This article proposes a broad class of mixture models constructed using suitable kernels on a general compact metric space and then on the planar shape space in particular. Following a Bayesian approach with a nonparametric prior on the mixing distribution, conditions are obtained under which the Kullback-Leibler property holds, implying large support and weak posterior consistency. Gibbs sampling methods are developed for posterior computation, and the methods are applied to problems in density estimation and classification with shape-based predictors. Simulation studies show improved estimation performance relative to existing approaches.

BackgroundAn important role for synovial pathology in the initiation and progression of knee osteoarthritis has been emphasised recently. This study aimed to examine whether ultrasonography-detected synovial changes associate with knee pain (KP) in a community population.MethodsA case–control study was conducted to compare people with early KP (n = 298), established KP (n = 100) or no KP (n = 94) at baseline. Multinomial logistic regression was used to estimate the odds ratio (OR) and 95% confidence interval (CI) between groups adjusted for radiographic osteoarthritis (ROA) severity and other confounding factors. After 1 year, 255 participants with early and established KP completed the follow-up questionnaire for changes in KP. Logistic regression with adjustment was used to determine predictors of KP worsening.ResultsAt baseline, effusion was associated with early KP (OR 2.64, 95% CI 1.57–4.45) and established KP (OR 5.07, 95% CI 2.74–9.38). Synovial hypertrophy was also associated with early KP (OR 5.43, 95% CI 2.12–13.92) and established KP (OR 13.27, 95% CI 4.97–35.43). The association with effusion diminished when adjusted for ROA. Power Doppler signal was uncommon (early KP 3%, established KP 2%, controls 0%). Baseline effusion predicted worsening of KP at 1 year (OR 1.95, 95% CI 1.05–3.64). However, after adjusting for ROA, the prediction was insignificant (adjusted OR 0.95, 95% CI 0.44–2.02).ConclusionsUltrasound effusion and synovial hypertrophy are associated with KP, but only effusion predicts KP worsening. However, the association/prediction is not independent from ROA. Power Doppler signal is uncommon in people with KP. Further study is needed to understand whether synovitis is directly involved in different types of KP.Electronic supplementary materialThe online version of this article (doi:10.1186/s13075-017-1486-7) contains supplementary material, which is available to authorized users.