EBM Resource Center

• Caplan, A. M., & Caplan, L. (2022). The GRADE Method. Rheum Dis Clin North Am, 48(3), 589-599. https://doi.org/10.1016/j.rdc.2022.04.002
• Chalmers, I. & Fox, D. (2016). Increasing the incidence and influence of systematic reviews on health policy and practice. American Journal of Public Health, 106(1), 11-13. doi:10.2105/AJPH.2015.302915 Why do people make practice and policy decisions in health care and public health without reference to relevant research, or only to biased samples of relevant research evidence? This illogical behavior doesn’t serve the interests of health service users or the public, yet it remains usual. One reason is that most reports of research do not help. Even very prestigious journals publish reports of new studies without acknowledging that readers need to know what the new evidence has added to the totality of trustworthy evidence relevant to the questions addressed.
• Djulbegovic, B., & Guyatt, G. H. (2017). Progress in evidence-based medicine: a quarter century on. Lancet, 390(10092), 415-423. https://doi.org/10.1016/s0140-6736(16)31592-6
• Fernandez, A., Sturmberg, J., Lukersmith, S., Madden, R., Torkfar, G., Colagiuri, R., & Salvador-Carulla, L. (2015). Evidence-based medicine: is it a bridge too far? Health Research Policy and Systems, 13(66). doi:10.1186/s12961-015-0057-0 This paper aims to describe the contextual factors that gave rise to evidence-based medicine (EBM), as well as its controversies and limitations in the current health context. Our analysis utilizes two frameworks: (1) a complex adaptive view of health that sees both health and healthcare as non-linear phenomena emerging from their different components; and (2) the unified approach to the philosophy of science that provides a new background for understanding the differences between the phases of discovery, corroboration, and implementation in science.
• Garritty, C., Stevens, A., Hamel, C., Golfam, M., Hutton, B., & Wolfe, D. (2019). Knowledge Synthesis in Evidence-Based Medicine. Semin Nucl Med, 49(2), 136-144. https://doi.org/10.1053/j.semnuclmed.2018.11.006
• Goldacre, B. & Gray, J. (2016). OpenTrials: towards a collaborative open database of all available information on all clinical trials. Trials, 17(164). doi:10.1186/s13063-016-1290-8 OpenTrials is a collaborative and open database for all available structured data and documents on all clinical trials, threaded together by individual trial. With a versatile and expandable data schema, it is initially designed to host and match the following documents and data for each trial: registry entries; links, abstracts, or texts of academic journal papers; portions of regulatory documents describing individual trials; structured data on methods and results extracted by systematic reviewers or other researchers; clinical study reports; and additional documents such as blank consent forms, blank case report forms, and protocols. The intention is to create an open, freely re-usable index of all such information and to increase discoverability, facilitate research, identify inconsistent data, enable audits on the availability and completeness of this information, support advocacy for better data and drive up standards around open data in evidence-based medicine. The project has phase I funding. This will allow us to create a practical data schema and populate the database initially through web-scraping, basic record linkage techniques, crowd-sourced curation around selected drug areas, and import of existing sources of structured and documents. It will also allow us to create user-friendly web interfaces onto the data and conduct user engagement workshops to optimise the database and interface designs. Where other projects have set out to manually and perfectly curate a narrow range of information on a smaller number of trials, we aim to use a broader range of techniques and attempt to match a very large quantity of information on all trials. We are currently seeking feedback and additional sources of structured data.
• Jassem, J. (2016). Bright and dark sides of evidence-based medicine. Polskie Archiwum Medycyny Wewnetrznej, 126(5), 347-352. doi:10.20452/pamw.3403 Evidence-based medicine is a current paradigm of practicing and teaching clinical medicine. This concept is widely believed to optimize clinical decision making by using the evidence derived from high-quality medical research, in particular from randomized clinical trials (RCTs) and their meta-analyses, as they are associated with a lower risk of bias compared with observational studies. However, RCTs are not devoid of limitations and their results may be distorted for various reasons. This article reviews the most common shortcomings of RCTs, with particular focus on publication bias.
• Kim, J. S. M.,..., & Weeks, L. (2022). Guidance on review type selection for health technology assessments: key factors and considerations for deciding when to conduct a de novo systematic review...Syst Rev 11: 206. doi: 10.1186/s13643-022-02071-7
• Malterud, K. (2019). The Impact of Evidence-Based Medicine on Qualitative Metasynthesis: Benefits to be Harvested and Warnings to be Given. Qual Health Res, 29(1), 7-17. https://doi.org/10.1177/1049732318795864
• Noyes, J.,... & Thomas, J. (2018). Cochrane Qualitative and Implementation Methods Group guidance series-paper 3: methods for assessing methodological limitations... Journal of Clinical Epidemiology, 97: 39-48. https://doi.org/10.1016/j.jclinepi.20
• Page, M. J.,... & Moher, D. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ, 372: 71. https://doi.org/10.1136/bmj.n71
• Shea, B. J., ... & Henry, D. A. (2017). AMSTAR 2: a critical appraisal tool for systematic reviews that include randomised or non-randomised studies...BMJ. 358: j4008. doi: 10.1136/bmj.j4008
• Wolff, R. F., ... & Mallett, S. (2019). PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies. Ann Intern Med, 170(1): 51-58. https://doi.org/10.7326/M18-1376
• Yi, T. W., Donnellan, S., & Levin, A. (2021). Evidence-Based Decision Making 4: Clinical Practice Guidelines. Methods Mol Biol, 2249: 455-466. https://doi.org/10.1007/978-1-0716-1138-8_24