Concerns, Healthcare Use, and Treatment Interruptions in Patients With Common Autoimmune Rheumatic Diseases During the COVID-19 Pandemic
Bibliographic record
Abstract
OBJECTIVE: To assess concerns and healthcare-related behaviors of patients with autoimmune rheumatic diseases during the coronavirus disease 2019 (COVID-19) pandemic. METHODS: Adults from the United States with rheumatoid arthritis (RA), psoriatic arthritis (PsA), ankylosing spondylitis (AS), and systemic lupus erythematosus (SLE) from the ArthritisPower Patient-Powered Research Network and CreakyJoints patient community completed surveys. Concerns and behaviors were compared among patients with different autoimmune conditions, disease-modifying antirheumatic drug (DMARD) use, and geographic measures of urban status, income, education, and COVID-19 activity. RESULTS: < 0.001). Avoidance of doctor's office visits (56.6%) or laboratory testing (42.3%) and use of telehealth (29.5%) were more common in urban areas. Among participants receiving a DMARD without COVID-19 or other respiratory illness, 14.9% stopped a DMARD, with 78.7% of DMARD interruptions not recommended by a physician. DMARD stopping was more common in participants with lower socioeconomic status (SES) and in participants who avoided an office visit (OR 1.46, 95% CI 1.04-2.04) or reported lack of telehealth availability OR 2.26 (95% CI 1.25-4.08). CONCLUSION: In the early months of the COVID-19 pandemic, patients with RA, PsA, AS, and SLE frequently avoided office visits and laboratory testing. DMARD interruptions commonly occurred without the advice of a physician and were associated with SES, office visits, and telehealth availability, highlighting the need for adequate healthcare access and attention to vulnerable populations during the pandemic.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 |
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".