COVID-19 Vaccine-Related Arthritis: A Descriptive Study of Case Reports on a Rare Complication
Bibliographic record
Abstract
Large-scale coronavirus disease 2019 (COVID-19) vaccination programs have been rolled out worldwide. Vaccines that are widely used globally include mRNA vaccines, adenoviral vector vaccines, and inactivated whole-virus vaccines. COVID-19 vaccines can lead to varying side effects. Among the most common of these adverse effects are pain at the injection site, fatigue, and headaches. Some side effects, however, are not very well documented, and these include joint-related adverse effects. In this review, we assess the epidemiology and clinical features of post-COVID-19 vaccination joint-related adverse effects based on the analysis of 16 patient case reports. Based on our analysis, we found that females formed the majority of the cases, accounting for 62.5% of patients, while 37.5% of the cases were males. The mean age of presentation among the patients was 54.8 years, with a standard deviation (SD) of 17.49 years. In 37.5% of the cases, patients received the Sinovac vaccine. The proportion of patients who received other vaccines was as follows: the Pfizer vaccine: 31.25%; Sputnik V: 12.5%; Moderna, AstraZeneca, and Covaxin: 6.25% each. The characteristics of joint-related adverse effects following COVID-19 vaccination were analyzed in this study. We identified several key findings related to factors such as age, gender, type of vaccine, clinical features, and diagnosis modality. Our analysis showed that more cases were reported among individuals who received the Sinovac vaccine, as compared to the others. Further research is required to examine the underlying cause of this association.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".