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Abstract Background Several surgical options for degenerative lumbar spinal stenosis (LSS) are available, but current guidelines do not recommend which one should be prioritized. Although previous network meta-analyses (NMAs) have been performed on this topic, they have major methodological problems and could not provide the convincing evidence and clinical practical information required. Methods Randomized controlled trials (RCTs) comparing at least two surgical interventions were included by searching AMED, CINAHL, EMBASE, the Cochrane Library, and MEDLINE (inception to August 2023). A frequentist random-effects NMA was performed for physical function and adverse events due to any reason. For physical function, three follow-up time points were included: short-term (

Abstract Background The prevalence of depression among people with chronic pain remains unclear due to the heterogeneity of study samples and definitions of depression. We aimed to identify sources of variation in the prevalence of depression among people with chronic pain and generate clinical prediction models to estimate the probability of depression among individuals with chronic pain. Methods Participants were from the UK Biobank. The primary outcome was a “lifetime” history of depression. The model’s performance was evaluated using discrimination (optimism-corrected C statistic) and calibration (calibration plot). Results Analyses included 24,405 patients with chronic pain (mean age 64.1 years). Among participants with chronic widespread pain, the prevalence of having a “lifetime” history of depression was 45.7% and varied (25.0–66.7%) depending on patient characteristics. The final clinical prediction model (optimism-corrected C statistic: 0.66; good calibration on the calibration plot) included age, BMI, smoking status, physical activity, socioeconomic status, gender, history of asthma, history of heart failure, and history of peripheral artery disease. Among participants with chronic regional pain, the prevalence of having a “lifetime” history of depression was 30.2% and varied (21.4–70.6%) depending on patient characteristics. The final clinical prediction model (optimism-corrected C statistic: 0.65; good calibration on the calibration plot) included age, gender, nature of pain, smoking status, regular opioid use, history of asthma, pain location that bothers you most, and BMI. Conclusions There was substantial variability in the prevalence of depression among patients with chronic pain. Clinically relevant factors were selected to develop prediction models. Clinicians can use these models to assess patients’ treatment needs. These predictors are convenient to collect during daily practice, making it easy for busy clinicians to use them.

Abstract Background There are increasing concerns that participants in health research in the UK are not representative of the UK population, risking widening health inequities. However, detailed information on the magnitude of the problem is limited. Therefore, we evaluated if the health research conducted in the Greater Manchester region was broadly representative of its diverse population. Methods We conducted an audit of all health research studies conducted exclusively in Greater Manchester, using data from a national research network. Two researchers selected studies that were (1) an interventional or observational study of a health outcome; (2) ‘closed’ for recruitment between May 2016 and May 2021 and (3) human research. They extracted study information (dates, contacts, sample recruited, clinical speciality). Participant characteristics were sourced from published and unpublished manuscripts and requested directly from principal investigators and named study contacts. Data were extracted, summarised and compared to the Greater Manchester population for the following metrics: ethnicity, sex, age, deprivation and smoking status. A weighted mean age estimate was calculated to account for variation in age reporting. Too few studies provided patient-level deprivation data so, using the area code of the recruitment site, the area level multiple deprivation, health deprivation and disability index and decile was derived. These data were geo-mapped using QGIS 3.26. Results Overall, 145/153 (95%) studies met inclusion criteria and participant information was sourced for 85/145 (59%) studies, representing 21,797 participants. Participant information was incomplete for all metrics. Where ethnicity (N = 10,259) data were available and compared to Greater Manchester estimates there was evidence that ethnic minorities were under-represented (6% versus 16%). Most of the recruitment occurred in central Manchester (50%) and with NHS hospital settings (74%). Conclusions Greater Manchester health research in 2016–2021 was centralised and under-represented ethnic minorities. We could not report which ethnic minority group was least represented because sourcing detailed participant information was challenging. Recommendations to improve the reporting of key participant characteristics with which to monitor representativeness in health research are discussed.