Evidence Based Point of Care/Clinical Decision Support Resources

The MSK Library has subscriptions to a number of resources that are often referred to as evidence based point of care tools or clinical decision support resources. A hallmark of these tools is that they often include succinct clinical information summaries accompanied by some strength of evidence rating or quality assessment and/or may offer links to evidence-based recommendations and guidelines. Some tools give users the opportunity to earn CME points and credits for spending time using the resource, while others provide separate content prepared specifically for clinicians while also offering versions intended for patients.

Included in the library’s Databases A-Z list, for example, are:
(click on name to access the resource) 

Note: To use these resources via the MSK Library’s subscription, you are not required to register for your own personal account – you can just use MSK’s institutional OpenAthens sign-on to gain access.

However, if you are interested in earning CME points and credits (in the tools that offer this option), you will need to register for a personal account after gaining access to the tool via MSK’s subscription.

Some tools also have a mobile app that you can download after you register for a personal account. If you experience any download issues or are prompted for MSK-specific subscription information that you do not have, please reach out to the MSK Library using our Ask Us form to obtain any needed subscription details from the library’s Content Team.

Discovering Clinical Trial Results: Is Searching ClinicalTrials.gov Necessary?

When considering which databases and other information sources to search as part of the methodology for a systematic review (SR) project, SR team members often must decide how comprehensive they wish to be in their efforts to maximize the discovery of clinical trial results.

In the March 30, 2023 issue of JAMA, a research letter entitled Comparison of Availability of Trial Results in ClinicalTrials.gov and PubMed by Data Source and Funder Type “examines the dissemination and timing of trial results by data source (i.e., ClinicalTrials.gov and PubMed) and funder type (i.e., NIH, non-NIH U.S. federal agency, industry, and other).”

See:
Nelson JT, Tse T, Puplampu-Dove Y, Golfinopoulos E, Zarin DA. Comparison of Availability of Trial Results in ClinicalTrials.gov and PubMed by Data Source and Funder Type. JAMA. 2023 Mar 30:e232351. doi: 10.1001/jama.2023.2351. Epub ahead of print. PMID: 36995689; PMCID: PMC10064282.

Key takeaway from this study:

“In this study, 39% of trials lacked results availability on ClinicalTrials.gov or PubMed after a minimum follow-up of 36 months following primary completion date. Nearly a quarter of all identified trial results were solely available on ClinicalTrials.gov, and 40% with available results were first available on ClinicalTrials.gov. Consistent with prior work, these findings suggest that searching both ClinicalTrials.gov and PubMed maximizes discovery of trial results.”

In addition to searching the clinical trial registry records directly from their respective native interfaces, for example, ClinicalTrials.gov or the International Clinical Trials Registry Platform (ICTRP), records from these sources are also included in Cochrane Central Register of Controlled Trials (CENTRAL) database, which is included as part of the Cochrane Library.

Learn more about the MSK Library’s Systematic Review Service or Ask Us at the MSK Library if you have any questions.

Cochrane RCT Classifier

As anyone who has worked on a systematic review (SR) project can attest – the record screening process can be a frustratingly tedious and time-consuming one. If available, most reviewers would likely welcome some kind of automation that streamlines and potentially reduces the manual record screening portion of their SR workload.

What is an RCT classifier algorithm?

An RCT classifier algorithm is “a tool to help you sort out the non-RCTs so that you can focus your effort on studies more likely to be included in your review”. In other words, researchers working on SRs that specify in their protocol that only studies reporting on RCTs will be included can now take advantage of tools that help them predict – using an automated algorithm derived from machine learning – whether a study is using a possible RCT or a not an RCT study design. 

The research team behind the leading RCT classifier algorithm tool (which includes members of the EPPI-Centre and Cochrane) published a paper in May 2022 describing the development and evaluation of their tool:

Thomas J, McDonald S, Noel-Storr A, Shemilt I, Elliott J, Mavergames C, Marshall IJ. Machine learning reduced workload with minimal risk of missing studies: development and evaluation of a randomized controlled trial classifier for Cochrane Reviews. J Clin Epidemiol. 2021 May;133:140-151. doi: 10.1016/j.jclinepi.2020.11.003. Epub 2020 Nov 7. PMID: 33171275; PMCID: PMC8168828. 

The good news for our MSK community is that this RCT classifier functionality has already been incorporated into Covidence, the systematic review project management system that the MSK Library subscribes to and provides access to. To turn this function on in a review that they are working on, a team member will need to have first selected the “Medical and health sciences” option under the “Area of Research” drop-down menu. After choosing to create this kind of review, the option to “Automatically tag studies reporting on RCTs using the Cochrane RCT Classifier” will become visible for a user to decide to enable of not. If enabled (only works with titles that have >15 characters and abstracts that have >400 characters), their SR records will be tagged as “Possible RCT” or “Not RCT” and can be filtered accordingly.

Learn more about “How to tag studies not reporting on RCTs” by checking out this article from the Covidence knowledgebase.

Questions? Ask Us at the MSK Library.