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JournalISSN: 2044-6055

BMJ Open 

BMJ
About: BMJ Open is an academic journal published by BMJ. The journal publishes majorly in the area(s): Medicine & Population. It has an ISSN identifier of 2044-6055. It is also open access. Over the lifetime, 30457 publications have been published receiving 475453 citations. The journal is also known as: British medical journal open.


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Journal ArticleDOI
01 Jan 2013-BMJ Open
TL;DR: The data presented display that patient experience is positively associated with clinical effectiveness and patient safety, and support the case for the inclusion of patient experience as one of the central pillars of quality in healthcare.
Abstract: Objective: To explore evidence on the links between patient experience and clinical safety and effectiveness outcomes. Design: Systematic review. Setting: A wide range of settings within primary and secondary care including hospitals and primary care centres. Participants: A wide range of demographic groups and age groups. Primary and secondary outcome measures: A broad range of patient safety and clinical effectiveness outcomes including mortality, physical symptoms, length of stay and adherence to treatment. Results: This study, summarising evidence from 55 studies, indicates consistent positive associations between patient experience, patient safety and clinical effectiveness for a wide range of disease areas, settings, outcome measures and study designs. It demonstrates positive associations between patient experience and self-rated and objectively measured health outcomes; adherence to recommended clinical practice and medication; preventive care (such as health-promoting behaviour, use of screening services and immunisation); and resource use (such as hospitalisation, length of stay and primary-care visits). There is some evidence of positive associations between patient experience and measures of the technical quality of care and adverse events. Overall, it was more common to find positive associations between patient experience and patient safety and clinical effectiveness than no associations. Conclusions: The data presented display that patient experience is positively associated with clinical effectiveness and patient safety, and support the case for the inclusion of patient experience as one of the central pillars of quality in healthcare. It supports the argument that the three dimensions of quality should be looked at as a group and not in isolation. Clinicians should resist sidelining patient experience as too subjective or mood-oriented, divorced from the ‘real’ clinical work of measuring safety and effectiveness.

1,509 citations

Journal ArticleDOI
01 Nov 2016-BMJ Open
TL;DR: The rationale for each of the 30 items on the STARD 2015 checklist is clarified, and what is expected from authors in developing sufficiently informative study reports is described.
Abstract: Diagnostic accuracy studies are, like other clinical studies, at risk of bias due to shortcomings in design and conduct, and the results of a diagnostic accuracy study may not apply to other patient groups and settings. Readers of study reports need to be informed about study design and conduct, in sufficient detail to judge the trustworthiness and applicability of the study findings. The STARD statement (Standards for Reporting of Diagnostic Accuracy Studies) was developed to improve the completeness and transparency of reports of diagnostic accuracy studies. STARD contains a list of essential items that can be used as a checklist, by authors, reviewers and other readers, to ensure that a report of a diagnostic accuracy study contains the necessary information. STARD was recently updated. All updated STARD materials, including the checklist, are available at http://www.equator-network.org/reporting-guidelines/stard. Here, we present the STARD 2015 explanation and elaboration document. Through commented examples of appropriate reporting, we clarify the rationale for each of the 30 items on the STARD 2015 checklist, and describe what is expected from authors in developing sufficiently informative study reports.

1,217 citations

Journal ArticleDOI
01 Dec 2016-BMJ Open
TL;DR: The AXIS tool was developed in a way that it can be used across disciplines to aid the inclusion of CSSs in systematic reviews, guidelines and clinical decision-making in a number of different disciplines.
Abstract: Objectives: The aim of this study was to develop a critical appraisal (CA) tool that addressed study design and reporting quality as well as the risk of bias in cross-sectional studies (CSSs). In addition, the aim was to produce a help document to guide the non-expert user through the tool. Design: An initial scoping review of the published literature and key epidemiological texts was undertaken prior to the formation of a Delphi panel to establish key components for a CA tool for CSSs. A consensus of 80% was required from the Delphi panel for any component to be included in the final tool. Results: An initial list of 39 components was identified through examination of existing resources. An international Delphi panel of 18 medical and veterinary experts was established. After 3 rounds of the Delphi process, the Appraisal tool for Cross-Sectional Studies (AXIS tool) was developed by consensus and consisted of 20 components. A detailed explanatory document was also developed with the tool, giving expanded explanation of each question and providing simple interpretations and examples of the epidemiological concepts being examined in each question to aid non-expert users. Conclusions: CA of the literature is a vital step in evidence synthesis and therefore evidence-based decision-making in a number of different disciplines. The AXIS tool is therefore unique and was developed in a way that it can be used across disciplines to aid the inclusion of CSSs in systematic reviews, guidelines and clinical decision-making.

1,013 citations

Journal ArticleDOI
01 Jan 2012-BMJ Open
TL;DR: To review published studies in representative populations with total hip or knee replacement for the treatment of osteoarthritis reporting proportions of people by pain intensity, there is an urgent need to improve general awareness of this possibility and to address determinants of good and bad outcomes.
Abstract: Background Total hip or knee replacement is highly successful when judged by prosthesis-related outcomes. However, some people experience long-term pain. Objectives To review published studies in representative populations with total hip or knee replacement for the treatment of osteoarthritis reporting proportions of people by pain intensity. Data sources MEDLINE and EMBASE databases searched to January 2011 with no language restrictions. Citations of key articles in ISI Web of Science and reference lists were checked. Study eligibility criteria, participants and interventions Prospective studies of consecutive, unselected osteoarthritis patients representative of the primary total hip or knee replacement population, with intensities of patient-centred pain measured after 3 months to 5-year follow-up. Study appraisal and synthesis methods Two authors screened titles and abstracts. Data extracted by one author were checked independently against original articles by a second. For each study, the authors summarised the proportions of people with different severities of pain in the operated joint. Results Searches identified 1308 articles of which 115 reported patient-centred pain outcomes. Fourteen articles describing 17 cohorts (6 with hip and 11 with knee replacement) presented appropriate data on pain intensity. The proportion of people with an unfavourable long-term pain outcome in studies ranged from about 7% to 23% after hip and 10% to 34% after knee replacement. In the best quality studies, an unfavourable pain outcome was reported in 9% or more of patients after hip and about 20% of patients after knee replacement. Limitations Other studies reported mean values of pain outcomes. These and routine clinical studies are potential sources of relevant data. Conclusions and implications of key findings After hip and knee replacement, a significant proportion of people have painful joints. There is an urgent need to improve general awareness of this possibility and to address determinants of good and bad outcomes.

1,000 citations

Journal ArticleDOI
01 Jul 2016-BMJ Open
TL;DR: The prediction interval reflects the variation in treatment effects over different settings, including what effect is to be expected in future patients, such as the patients that a clinician is interested to treat, in meta-analyses.
Abstract: Objectives Evaluating the variation in the strength of the effect across studies is a key feature of meta-analyses. This variability is reflected by measures like τ 2 or I 2 , but their clinical interpretation is not straightforward. A prediction interval is less complicated: it presents the expected range of true effects in similar studies. We aimed to show the advantages of having the prediction interval routinely reported in meta-analyses. Design We show how the prediction interval can help understand the uncertainty about whether an intervention works or not. To evaluate the implications of using this interval to interpret the results, we selected the first meta-analysis per intervention review of the Cochrane Database of Systematic Reviews Issues 2009–2013 with a dichotomous (n=2009) or continuous (n=1254) outcome, and generated 95% prediction intervals for them. Results In 72.4% of 479 statistically significant (random-effects p 2 >0), the 95% prediction interval suggested that the intervention effect could be null or even be in the opposite direction. In 20.3% of those 479 meta-analyses, the prediction interval showed that the effect could be completely opposite to the point estimate of the meta-analysis. We demonstrate also how the prediction interval can be used to calculate the probability that a new trial will show a negative effect and to improve the calculations of the power of a new trial. Conclusions The prediction interval reflects the variation in treatment effects over different settings, including what effect is to be expected in future patients, such as the patients that a clinician is interested to treat. Prediction intervals should be routinely reported to allow more informative inferences in meta-analyses.

874 citations

Performance
Metrics
No. of papers from the Journal in previous years
YearPapers
20232,197
20225,205
20213,896
20203,576
20194,227
20182,579