Back

Effects of the COVID-19 Pandemic on Individuals with Fibromyalgia - a Systematic Scoping Review Protocol

Sahar, T.; Jalali, A.; Toupin, S.; Verner, M.; Mitrovic, S.; Minerbi, A.; Shir, Y.; Fitzcharles, M.-A.; Page, G.

2023-01-31 pain medicine
10.1101/2023.01.27.23284843 medRxiv
Show abstract

ObjectiveThe objectives of this review are to systematically search databases and identify studies that examined the effects of COVID-19 pandemic on symptomatology of adults who had fibromyalgia prior to the pandemic, in order to map the existing knowledge and identify knowledge gaps. IntroductionThe COVID-19 pandemic has affected people worldwide in multiple ways. Some suffered infection of varying severity and many experienced stressors associated with quarantine restrictions, lockdowns, and the consequences of social distancing. An initial literature search indicates that the pandemic had different and sometime contradicting effects on individuals with fibromyalgia; while some people experienced worsening of symptoms, others reported symptom relief because of the reduced pace and demands of daily life. Inclusion criteriaAny studies that explored the experience of adults with fibromyalgia syndrome during the COVID-19 pandemic. We will review only studies with participants who were diagnosed with fibromyalgia prior to the pandemic. MethodsFollowing a pilot search, we developed a full search strategy for Medline, Embase, CINAHL and PsycInfo. The reference list of all included sources of evidence will be screened for additional studies. Sources of unpublished studies to be searched: clinical trial.gov, OPENGREY.EU and MedRxiv. Studies in any language will be included. Abstracts will be screened for inclusion by two reviewers. Similarly, two independent reviewers will systematically extract the data from the included articles. Disagreements in any stage will be resolved through consensus. The results will be presented in tables and will be accompanied by a narrative analysis.

Matching journals

The top 1 journal accounts for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.