In-Person and Virtual Clinic Visit Frequency to Rheumatologists for Rheumatoid Arthritis at an Academic Medical Center Before, During, and After COVID Lockdown
Bibliographic record
Abstract
INTRODUCTION: This study aimed to describe outpatient visit volume in a subspecialty clinic before, during, and after COVID lockdown. METHODS: We assessed monthly in-person and virtual visit volume (telephone-only or video) of 257 patients with rheumatoid arthritis (RA) at one academic center before, during, and post COVID lockdown, November 2018 to September 2021. The primary outcome was monthly visit volume to a rheumatologist. Visit volume, visit type (in-person vs. virtual), and annual visit frequency per patient were assessed. Piecewise Poisson regression models were constructed to examine visit volume trends. Predictors of patient's visit volume before and after the lockdown were examined using multivariable linear regression. RESULTS: Median patient age was 58 years; 84% were female; 82% used any disease-modifying anti-rheumatic drug (DMARD), and 62% used a targeted or biologic DMARD. Visit volume was stable 18 months prior to the COVID pandemic [slope 1.00 (95% confidence interval (CI) 0.99-1.01)] and increased at a rate of 2% per month post-lockdown [1.02 (95% CI 1.01-1.03)]. In-person visit volume was greatly reduced during the lockdown, with 61% virtual (51% video, 10% telephone). In the 18 months after lockdown, visit volume rebounded to pre-pandemic levels and continued to increase, with 11% virtual. Older age, serologic status, use of combination DMARDs, and non-steroidal anti-inflammatory drug (NSAID) use predicted greater visit volume during the pre-lockdown period. No variables predicted visit volume post-lockdown. CONCLUSION: While COVID caused a huge disruption in rheumatology practice, visit volume for RA rebounded in one American academic center, with an increasing slope in visit volume after lockdown.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".