Frequency of Symptomatic Adverse Events in Rheumatoid Arthritis: An Exploratory Online Survey
Bibliographic record
Abstract
OBJECTIVE: To generate initial data on the frequency and effect of symptomatic adverse events (AEs) associated with rheumatoid arthritis (RA) drug therapy from the patient perspective. METHODS: We conducted an exploratory online survey asking patients with RA to indicate whether they currently or had ever experienced the 80 different symptomatic AEs included in the Patient-Reported Outcomes of the Common Terminology Criteria for Adverse Events (PRO-CTCAE). Results were summarized to report their frequency, and regression models were used to estimate their associations with RA medication use and overall bother. RESULTS: The 560 patients who completed the survey and reported taking ≥ 1 RA medication (disease-modifying antirheumatic drugs [DMARDs], steroids, nonsteroidal antiinflammatory drugs [NSAIDs]), had a mean disease duration of 8 years, and were on a wide range of DMARDs. The number of symptomatic AEs experienced in the past 7 days was none (6%), 1-10 (28%), 11-20 (28%), and > 20 (38%). Overall, most participants reported that side effects bothered them somewhat (28%), quite a bit (24%), or very much (15%). In multivariable regression analyses, current prednisone and NSAID use were associated with the greatest number of current side effects (26 and 22, respectively). Many of the strongest associations between current symptomatic AEs and medication use aligned with known side effect profiles. CONCLUSION: In this exploratory online survey, patients with RA reported frequent symptomatic AEs with their medications that are bothersome. Further work is needed to develop and validate a measure for use in patients with rheumatic disease.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".