![]() The main objective of the filter is to achieve maximum sensitivity so as not to lose any relevant studies when using the filter, while maintaining specificity to make the search more efficient. The aim of this paper is to develop and evaluate a search filter for prognostic factor studies to be used in SRs. While carrying out various PF systematic reviews we explored the possibility of using a PF filter, however, to the best of our knowledge, no filter exists for these studies. These published prognostic search filters have lower sensitivity and precision than other types of search filters such as those for medical intervention studies. Generic filters exist for finding prediction and prognosis studies such as the Haynes broad filter, Ingui filter and the Yale prognosis and natural history filter. A prognostic factor (PF) “is any measure that, among people with a given health condition (that is, a start point), is associated with a subsequent clinical outcome (an endpoint)”. Prognosis research can be classified into four different themes or areas of research: fundamental prognostics, prognostic models, stratified medicine, and prognostic factors. This area of clinical research is becoming significantly more important, as throughout the world, people are living longer, but with more chronic health conditions and diseases. Prognosis research focuses on identifying variables that allow the estimation of the possibilities of improvement or worsening of a given health problem. Due to these limitations, the use of filters in diagnostic or prognostic studies is not widely recommended. These studies also suffer from poorer indexing of terms, thus making them more difficult to find in the database. Unlike RCTs, non-interventional investigations have heterogenous, non-standardized study designs. These filters have been successful in reducing the number of references needed to screen in SRs, however this is difficult to reproduce for prognostic factor studies, as the literature pertaining to non-interventional studies is more variable. Dozens of search filters exist for retrieving randomized controlled trials (RCTs). A search filter is a pre-defined combination of search terms combined into a search strategy using the “AND” Boolean operator. To overcome this, methodological search filters have been developed to find articles related to specific clinical questions. However, searches can often retrieve an overwhelming number of studies. It is essential to carry out a systematic and extensive search for any type of systematic review (SR). The specificity of the filter could be improved by defining additional terms to be included, although it is important to evaluate any modification to guarantee the filter is still highly sensitive. Using this filter could save as much as 40% of the title and abstract screening task. We developed a search filter for OVID-Medline with acceptable performance that could be used in systematic reviews of PF studies. The time saved by using the filter ranged from 13–70%. The NNR (number needed to read) value varied largely from 6 to 278. The precision of the filter varied considerably, ranging from 0.36 to 17%. The overall sensitivity of the filter was calculated to be 95%, while the overall specificity was 41%. We then evaluated the filter using the relative recall method against the reference set, comparing the references included in the SRs with our new search using the filter. The consecutive connection of these terms with the Boolean operator OR produced the filter. This list of terms was evaluated by the Delphi panel for inclusion in the filter, resulting in a final set of 8 appropriate terms. After completing a word frequency analysis using the reference set studies, we compiled a list of 80 of the frequent methodological terms. We constructed a reference set of 73 studies included in six systematic reviews from a larger sample. We evaluated filter performance using the relative recall methodology. We followed current recommendations for the development of a search filter by first identifying a reference set of PF studies included in relevant systematic reviews on the topic, and by selecting search terms using a word frequency analysis complemented with an expert panel discussion. We aimed to develop and evaluate a search filter to identify PF studies in Ovid MEDLINE that has maximum sensitivity. To our knowledge, no filter exists for finding studies on the role of prognostic factor (PF). Methodological search filters have been developed to find articles related to specific clinical questions. However, finding all information to include in systematic reviews can be challenging. Systematic reviews (SRs) are valuable resources as they address specific clinical questions by summarizing all existing relevant studies.
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