ArticleBMJ open2025
Comparative Efficacy and Safety of Pharmacological Agents for Chronic Low Back Pain: A Systematic Review and Bivariate Bayesian Network Meta-analysis Protocol.
Article in BMJ open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Clinical evaluation of deep learning accelerated lumbar T2-weighted and fat-suppressed MRI sequences.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- Integrated Multiomics Analysis Reveals Manual Therapy Restores Autophagic Homeostasis via the AMPK/ULK1 Axis in Chronic Low Back Pain.Pain research & management · 2026Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionNon-specific chronic low back pain is a leading cause of disability-adjusted life-years (DALYs) with a lifetime prevalence of 80%-85%. Although many modalities of treating chronic low back pain are available, pharmacotherapy remains a very important treatment modality, according to many recognised guidelines. Each of these pharmacological agents has evidence supporting its efficacy in treating chronic low back pain, and each of them is associated with side effects. At present, no study has unified these two outcomes or ranked them from the most effective to the least effective. Thus, this study aims to identify pharmacological agents that rank best by conducting multivariate bivariate network meta-analysis.
methodsFour databases (Medline/Pubmed, Cochrane Central Register for Controlled Trials, Cochrane Database for Systematic Reviews and CINAHL) will be systematically searched from their inception to 31 July 2024 for randomised control trials that meet the inclusion criteria. Appropriate data will be extracted from the relevant studies, including the two outcomes of interest: the pain intensity score and the proportion of patients who withdrew from the study because of adverse events. A bivariate Bayesian network meta-analysis will be used to combine the two outcomes of interest to form a single probability that will rank the pharmacotherapy agents from the most effective and safe to the least. The studies will be assessed for risk of bias, and their evidence will be graded using the Grading of Recommendations Assessment, Development, and Evaluation assessment tool. Inconsistency, which is a key assumption of network meta-analysis, will be assessed using both local and global methods. ETHICS AND DISSEMINATION: This systematic review is based on already published data, so it does not require ethical approval. The findings from the review, after completion, will be disseminated via peer-reviewed publications. PROSPERO REGISTRATION NUMBER: CRD42024569192.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.