Rheumatoid arthritis and COVID-19 outcomes: a systematic review and Meta-analysis
Bibliographic record
Abstract
OBJECTIVES: This study aimed to conduct a comprehensive systematic literature review and meta-analysis to assess the risk and outcomes of coronavirus disease 2019 (COVID-19) in patients with rheumatoid arthritis. METHODS: A systematic search was performed across four electronic databases. The quality of the studies was assessed using the Newcastle‒Ottawa quality assessment scale and the Joanna Briggs Institute critical appraisal checklist. Statistical analyses were conducted using STATA 14 software. RESULTS: A total of 62 studies were included in the analysis. First, the meta-analysis revealed the following prevalence rates among rheumatoid arthritis patients: COVID-19, 11%; severe COVID-19, 18%; COVID-19-related hospitalization, 29%; admission to the intensive care unit (ICU) due to COVID-19, 10%; and death from COVID-19, 8%. Second, rheumatoid arthritis was associated with an increased risk of COVID-19 infection (OR 1.045(0.969-1.122), p = 0.006), COVID-19-related hospitalization (OR 1.319(1.055-1.584), p = 0.006), admission to the ICU due to COVID-19 (OR 1.498(1.145-1.850), p = 0.002), and death from COVID-19 (OR 1.377(1.168-1.587), p = 0.001). Third, no statistically significant association was found between rheumatoid arthritis and severe COVID-19 (OR 1.354(1.002-1.706), p = 0.135). CONCLUSIONS: Rheumatoid arthritis patients have a significantly greater risk of COVID-19 infection, hospitalization, ICU admission, and death than individuals without rheumatoid arthritis. However, rheumatoid arthritis did not show a significant association with the risk of severe COVID-19. These findings underscore the need for tailored management strategies and vigilant monitoring of COVID-19 outcomes in rheumatoid arthritis patients. SYSTEMATIC REVIEW REGISTRATION: The study has been registered on PROSPERO [ https://www.crd.york.ac.uk/PROSPERO/ ], and the registration number is CRD42024528119.
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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 | 0.021 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.045 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".