The Journal of Pediatric Nursing: Nursing Care of Children and Families (JPN) aims to publish evidence-based practice, quality improvement, theory, and research papers on healthy and ill infants, children, and adolescents. JPN also features the regular column “Hot Topics and Technology,” for which authors may submit brief papers.
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.
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:
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.
Systematic Review (SR) searching adopts both systematic and comprehensive approaches with the goal of retrieving, ideally, all the literature relevant to the focused question at the base of your Systematic Review. Typically, an expert searcher, such as an information professional, uses a combination of keywords qualified with field tags (e.g. [tiab] field tag in PubMed related to title and abstract fields of PubMed records) and subject headings (e.g. MeSH in PubMed and EmTree in Embase) for SR searching.
When selecting the terms for an SR, it is best to focus strictly on the terms directly related to the subject or clinical question being addressed. Occasionally it can be appropriate to expand the search to a slightly broader focus to retrieve literature where the exact subject matter may be discussed within the context of other subjects within a broader question.
An example of this expanded search: If the SR is focused on breast cancer surgery, a broader focus would be to look at any/all cancer surgeries wherein breast cancer specific surgery may be discussed.
All search approaches, whether broad or narrow, must be reflected in transparent and reproducible documented search strategies. It is important to remember that there is no “perfect” comprehensive search strategy that will only retrieve relevant citations. It is to be expected that any search, especially a comprehensive SR search, will retrieve many more citations than are actually relevant to the question being asked. Part of the SR process is excluding these irrelevant citations through multiple steps, explained in PRISMA.
However, there is another category besides relevant and irrelevant results, that is typically retrieved – these citations are related to aspects of your topic you did not consider when asking your clinical question and devising your search strategies. These “by-products” might appear important enough that they should be included in your review, but this will be deterring from your original question.
Example: Your SR is on cancer patients’ attitude to health. You devise a comprehensive search strategy and include relevant search terms. As you begin screening the retrieved citations you realize that many of the articles actually focus on health education as it relates to attitudes. You may want to simply add health education as an additional aspect of your SR since it appears to be a valuable aspect of cancer patients’ health attitudes.
The issue with this approach is that unless you backtrack and revise your clinical question and search strategies (and thus essentially starting over from the beginning), your results and conclusions would deviate from the actual question that was proposed initially. If health education was not addressed in your original clinical question and reflected in your search strategies, it would be improper to include it in the final SR as there is likely an entire body of literature that was missed and thus any systematic conclusions could not be made regarding it.
Instead, these “by-product” citations (health education articles that came up in search results for health attitudes) should be treated as irrelevant to the systematic review you are conducting. A potential solution could be mentioning in the discussion section that from this review it was discovered that education is strongly tied to cancer patients’ attitudes toward their disease and their health and that it would be worthwhile to conduct a future review looking at how education can impact these views.
Takeaway: Try not to include “by-product” topics in your final review and analysis.