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Background and Aims: Numerous endometriosis treatments exist, ranging from surgical to traditional medications to complementary and alternative medicines (CAMs). The ranking of treatments across management categories remains unknown. To address this research gap, this network meta-analysis (NMA) aims to rank endometriosis treatments in terms of efficacy and safety for endometriosis-related pain and quality of life. Method: A systematic literature search and fixed effects NMA was conducted. Eight databases were searched from inception to August 2022. All randomised control trials (RCTs) in women with surgically confirmed endometriosis were considered. Results: After screening 8,668 studies, 165 RCTs were included, consisting of 26,762 participants, investigating 48 medical, 13 CAMs and 16 surgical interventions. Preliminary analysis of the CAMs interventions (9 RCTs, n=522) suggested acupuncture (SMD -1.96; 95%CI: -2.63, -1.29), olive oil (SMD -1.68; 95%CI: -2.69, -0.67), fatty acid supplements (SMD -1.48; 95% CI: -2.15, -0.81), contraception + Chinese medicines (SMD -1.28; 95%CI: -2.39, -0.16), Chinese medicine (SMD -0.91, 95%CI: -1.78, -0.04) and vitamin C&E (SMD -0.38; 95%CI -0.89, 0.13) demonstrated a reduction in pain compared to placebo. There was insufficient evidence to detect a difference in pain using Vitamin D compared to placebo (SMD 0.09 95% CI: -0.55, 0.73). Complete results, including the individual and combined categories within a Bayesian framework and intervention ranking, will be available for ASPIRE 2023. Conclusion: This is the first NMA to compare the evidence of all endometriosis treatments. The findings have the potential to guide treatment decisions and guideline development.

There are important differences in the risk of SARS-CoV-2 infection and death depending on occupation. Infections in healthcare workers have received the most attention, and there are clearly increased risks for intensive care unit workers who are caring for COVID-19 patients. However, a number of other occupations may also be at an increased risk, particularly those which involve social care or contact with the public. A large number of data sets are available with the potential to assess occupational risks of COVID-19 incidence, severity, or mortality. We are reviewing these data sets as part of the Partnership for Research in Occupational, Transport, Environmental COVID Transmission (PROTECT) initiative, which is part of the National COVID-19 Core Studies. In this report, we review the data sets available (including the key variables on occupation and potential confounders) for examining occupational differences in SARS-CoV-2 infection and COVID-19 incidence, severity and mortality. We also discuss the possible types of analyses of these data sets and the definitions of (occupational) exposure and outcomes. We conclude that none of these data sets are ideal, and all have various strengths and weaknesses. For example, mortality data suffer from problems of coding of COVID-19 deaths, and the deaths (in England and Wales) that have been referred to the coroner are unavailable. On the other hand, testing data is heavily biased in some periods (particularly the first wave) because some occupations (e.g. healthcare workers) were tested more often than the general population. Random population surveys are, in principle, ideal for estimating population prevalence and incidence, but are also affected by non-response. Thus, any analysis of the risks in a particular occupation or sector (e.g. transport), will require a careful analysis and triangulation of findings across the various available data sets.

Health interventions should be tested before being introduced into clinical practice, to find out whether they work and whether they are harmful. However, research studies will only provide reliable answers to these questions if they are appropriately designed and analysed. But these are not trivial tasks. We review some methodological challenges that arise when evaluating fertility interventions and explain the implications for a non-statistical audience. These include flexibility in outcomes and analyses; use of surrogate outcomes instead of live birth; use of inappropriate denominators; evaluating cumulative outcomes and time to live birth; allowing each patient or couple to contribute to a research study more than once. We highlight recurring errors and present solutions. We conclude by highlighting the importance of collaboration between clinical and methodological experts, as well as people with experience of subfertility, for realising high-quality research.

There are important differences in the risk of SARS-CoV-2 infection and death depending on occupation. Infections in healthcare workers have received the most attention, and there are clearly increased risks for intensive care unit workers who are caring for COVID-19 patients. However, a number of other occupations may also be at an increased risk, particularly those which involve social care or contact with the public. A large number of data sets are available with the potential to assess occupational risks of COVID-19 incidence, severity, or mortality. We are reviewing these data sets as part of the Partnership for Research in Occupational, Transport, Environmental COVID Transmission (PROTECT) initiative, which is part of the National COVID-19 Core Studies. In this report, we review the data sets available (including the key variables on occupation and potential confounders) for examining occupational differences in SARS-CoV-2 infection and COVID-19 incidence, severity and mortality. We also discuss the possible types of analyses of these data sets and the definitions of (occupational) exposure and outcomes. We conclude that none of these data sets are ideal, and all have various strengths and weaknesses. For example, mortality data suffer from problems of coding of COVID-19 deaths, and the deaths (in England and Wales) that have been referred to the coroner are unavailable. On the other hand, testing data is heavily biased in some periods (particularly the first wave) because some occupations (e.g. healthcare workers) were tested more often than the general population. Random population surveys are, in principle, ideal for estimating population prevalence and incidence, but are also affected by non-response. Thus, any analysis of the risks in a particular occupation or sector (e.g. transport), will require a careful analysis and triangulation of findings across the various available data sets.

BackgroundScreening with cardiac non-invasive stress studies (NISS) prior to listing for kidney transplantation can help in identifying treatable coronary disease and is considered an integral part of pre-kidney transplant evaluation. However, few studies assessed their effectiveness in all patients evaluated for transplantation in clinical practice. To evaluate the role of NISS in pre-kidney transplant evaluation we analyzed their impact prior to waitlisting in 1053 adult CKD-5 patients consecutively evaluated in Greater Manchester, UK during a 6-year period.Methods918 waitlisted patients were grouped based on presence or absence of Diabetes or Cardio-Vascular Disease (CVD): Group-1 (255 DM-/CVD-/NISS-), Group-2 (368 DM-/CVD-/NISS+) and Group-3 (295 with DM or CVD).ResultsGroup-2 patients had longer 'time-to-listing' (5.5months in Group-1 vs 6.9months in 'Normal-NISS' vs 9.9months in 'Abnormal-NISS', pConclusionsPre-kidney transplant evaluation with NISS influenced clinical management in 19 of 1053 (2%) patients. Screening with NISS added limited benefit but contributes to significant delays in listing and adding resource implications. Further studies are needed to assess clinical and cost effectiveness of NISS in pretransplant evaluation to optimize outcomes and resources.