Accessing care during the pandemic: A UK wide survey of people with rheumatoid arthritis and adult juvenile inflammatory arthritis during the COVID‐19 pandemic
Bibliographic record
Abstract
COVID-19 drastically changed healthcare delivery models for rheumatology services. We sought to understand the impact of these changes for patients with Rheumatoid Arthritis (RA) and adult Juvenile Inflammatory Arthritis (AJIA) in established patients and those newly diagnosed during the pandemic. RESULTS: Of the 316 participants, a significant proportion regularly used analgesics (45.4%, n = 119), corticosteroids (17.9%, n = 47) and Non-Steroidal Anti-Inflammatory Drugs [(NSAIDs) (36.6%, n = 96)]. Two thirds of participants (66.5%, n = 210) did not know their Disease Activity Score-28 (DAS28). Of the remaining third, moderate disease activity (12%, n = 38) was most reported. We found that 16.8% (n = 53) felt their condition was managed well during the pandemic. The remainder felt more negatively. For the newly diagnosed cohort, 34.5% (n = 10) delayed seeking GP help because of COVID-19 concerns. Once assessed, a quarter (24.1%, n = 7) were referred to rheumatology after 4 or more consultations. We found 47% (n = 77) expressed positive opinions on remote consultations, whereas 36% (n = 59) had concerns. The lack of clinical examination (42.5%, n = 25) was flagged. Changing the dynamic from health worker to a patient centred approach was the most wished for improvement (20.3%, n = 64). CONCLUSIONS: Most participants did not know their disease activity status, which is of concern. With a push towards patient-centred and patient-led care, education and supported self-management is critically important. There is high use of NSAIDs and corticosteroids. Pathways of care underwent change with subsequent delays in specialist assessment. The introduction of patient-initiated follow-up (PIFU) and virtual consultations further distances healthcare professionals from patients and could affect outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".