Effects of the COVID-19 Pandemic on Individuals with Fibromyalgia – a Systematic Scoping Review Protocol
Bibliographic record
Abstract
Abstract Objective The 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. Introduction The 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 criteria Any 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. Methods Following 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 Sources of unpublished studies to be searched: clinical trial.gov, OPENGREY.EU and MedRxiv.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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.087 | 0.087 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.015 | 0.012 |
| Bibliometrics | 0.023 | 0.014 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.055 | 0.009 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".