Depression and anxiety are triggers for Rheumatoid Arthritis?A Systematic Review Protocol
Bibliographic record
Abstract
Background: Rheumatoid Arthritis is a systemic autoimmune disease that affects 1% of the world population. These patients have a higher prevalence of mental disorders. The bidirectional association between mental disorders and autoimmunity may be explained by the interaction of proinflammatory cytokines with specific areas of the brain. But to this date, no systematic review associates depression and anxiety as risk factors for Rheumatoid Arthritis. The aim of this systematic review is to analyze the current evidence on this issue. Methods: This protocol follows the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The search will be carried out in the following databases: Medline/PubMed, Embase, Scopus, Web of Science, LILACS, Cochrane Library, PsycInfo and ProQuest. We will include cohort and case-control studies of the adult population that make an association between depression, anxiety and Rheumatoid Arthritis. The extraction of the data will be performed after the full reading of the articles and inclusion based on the eligibility criteria. To assess the risk of bias the Newcastle-Ottawa Scale will be used. Discussion: The results of this systematic review seek to fill in the knowledge gaps in this area of psychoneuroimmunology and bring information to help specialists and researchers. Ethics and dissemination: Does not require ethics committee approval as the work involves searching data already published in existing databases. The electronic publication of this Systematic Review will be in a peer-reviewed journal. Systematic review record in PROSPERO: CRD42023404169
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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 | 0.059 | 0.062 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.021 | 0.013 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.095 | 0.010 |
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".