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• The way to facilitate access to scientific articles is share the results of their studies over the internet. • In order to measure the attention attracted online, a new score named Altmetric was created. • There are no studies that have analysed associated factors of Altmetric score for systematic reviews and clinical practice guidelines. Although Altmetric has been widely used by researchers to monitor the audience of their articles, there are no studies that have analysed factors associated with Altmetric score for systematic reviews and clinical practice guidelines. 1) To analyse factors that could be associated with Altmetric scores for low back pain systematic reviews and clinical practice guidelines. 2) To describe the characteristics of these articles and their Altmetric scores. We searched for all low back pain systematic reviews and guidelines indexed on the Physiotherapy Evidence Database published between 2015 and 2017. We extracted data related to the published paper, the publishing journal, and Altmetric scores. A total of 66 systematic reviews and 5 guidelines were included. The variable impact factor (independent variable) was associated with Altmetric mentioned score (dependent variable) with a β coefficient of 15.4 (95% CI: 0.97, 29.7) ajusted to all remaining variables. The variable number of citations normalized by year of publication (independent variable) was associated with Altmetric reader score (dependent variable) with a β coefficient of 6.4 (95% CI: 4.03, 8.72) ajusted to all remaining variables. We also found that the majority of the systematic reviews and guidelines were published in English, had a descriptive title, were published as open access, included multicenter studies, and had media release generated by the publishing journal. Metrics related to the number of citations, such as the impact factor are associated with Altmetric scores. [ABSTRACT FROM AUTHOR]

• We will monitor compliance with the intervention and adverse events. • Data will be analyzed following intention-to-treat principles. • A cost-effectiveness analysis will be performed. This a priori statistical analysis plan describes the methods of analysis for the Trial Of Prevention Strategies for low back pain (TOPS). TOPS aimed to investigate the effectiveness and cost-effectiveness of exercise and education classes compared with a minimal intervention control in preventing recurrence of low back pain (LBP) in people who have recently recovered from an episode of LBP. TOPS is a superiority, pragmatic, parallel-group randomized controlled trial with allocation concealment, blinded outcome assessors, and intention-to-treat analysis. Participants were randomized to a physical therapist-led exercise and education program for 12 weeks or minimal intervention. The primary outcome was days to recurrence of an episode of LBP. The three key secondary outcomes were days to recurrence of an episode of LBP resulting in (1) activity limitation, (2) care seeking for LBP; and (3) work absence of at least 1 day. Differences in survival curves for the primary (days to recurrence) and secondary outcome (days to LBP with activity limitation, days to care seeking due to LBP, and days to work absence due to LBP) will be compared using Cox regression. Hazard ratios (HRs) and median survival times with 95% confidence intervals (CI) will be calculated. The number of adverse events, including serious adverse events will be reported and the proportion of adverse events between groups will be compared using a Chi-squared test. This paper will provide a detailed description of the planned analyses for the TOPS trial. Australian New Zealand Clinical Trials Registry (ACTRN12615000939594). [ABSTRACT FROM AUTHOR]

Background: Statistical analysis plans increase the transparency of decisions made in the analysis of clinical trial results. The purpose of this paper is to detail the planned analyses for the PREVENT trial, a randomized, placebo-controlled trial of patient education for acute low back pain. Results: We report the pre-specified principles, methods, and procedures to be adhered to in the main analysis of the PREVENT trial data. The primary outcome analysis will be based on Mixed Models for Repeated Measures (MMRM), which can test treatment effects at specific time points, and the assumptions of this analysis are outlined. We also outline the treatment of secondary outcomes and planned sensitivity analyses. We provide decisions regarding the treatment of missing data, handling of descriptive and process measure data, and blinded review procedures. Conclusions: Making public the pre-specified statistical analysis plan for the PREVENT trial minimizes the potential for bias in the analysis of trial data, and in the interpretation and reporting of trial results. [ABSTRACT FROM AUTHOR]