The Association between COVID-19 and Reactive Arthritis: A Systematic Review of Case Reports and Case Series
Bibliographic record
Abstract
INTRODUCTION: Reactive arthritis (ReA) is a joint inflammation that follows an infection at a distant site, often in the gastrointestinal or urogenital tract. Since the emergence of COVID-19 in January 2020, several case reports have suggested a relation between reactive arthritis and severe acute respiratory syndrome coronavirus 2 (SARS-COV-2), due to the novelty of the disease, most findings were reported in the form of case reports or case series, and a comprehensive overview is still lacking. METHODS: We searched PubMed/Medline and Embase to identify studies addressing the association between ReA and COVID-19. The following terms were used: ("Reactive Arthritis" OR "Post-Infectious Arthritis" OR "Post Infectious Arthritis") AND ("COVID-19" OR "SARS-CoV-2" OR "2019-nCoV"). RESULTS: , 2022, were included in this study. A wide range of ages was affected (mean 41.0, min 4 max 78), with a higher prevalence of males (61.0%) from 16 countries. The number and location of the affected joints were different in included patients, with a higher prevalence of polyarthritis in 41.5% of all cases. Cutaneous manifestations and visual impairments were found as the most common associated symptoms. Most patients (95.1%) recovered, with a mean recovery time of 24 days. Moreover, arthritis induced by COVID-19 seems to relieve faster than ReA, followed by other infections. CONCLUSION: ReA can be a possible sequel of COVID-19 infection. Since musculoskeletal pain is a frequent symptom of COVID-19, ReA with rapid onset can easily be misdiagnosed. Therefore, clinicians should consider ReA a vital differential diagnosis in patients with post-COVID-19 joint swelling. Additional studies are required for further analysis and to corroborate these findings.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".