A population-based analysis of rheumatology care patterns for inflammatory arthritis during COVID-19 in Alberta, Canada
Bibliographic record
Abstract
OBJECTIVE: The aim of the study was to understand the impact of the COVID-19 pandemic on inflammatory arthritis (IA) rheumatology care in Alberta, Canada. METHODS: We used linked provincial health administrative datasets to establish an incident cohort of individuals with rheumatoid arthritis (RA), psoriatic arthritis (PsA) and Ankylosing Spondylitis (AS) seen at least once by a rheumatologist. We examined incidence rates (IR) per 100,000 population, and patterns of follow-up care between 2011 and 2022. In a subset of individuals diagnosed five years prior to the pandemic, we report on those lost to follow-up during the pandemic, and those with virtual care visits followed by in-person visit within 30 days. Multivariable logistic regression was used to examine patient characteristics associated with these patterns of care. RESULTS: The IR for RA in 2020 declined compared to previous years (44.6), but not for AS (9.2) or PsA (9.1). In 2021 IRs rose (RA 49.5; AS 11.8; PsA 11.8). Among those diagnosed within 5 years of the pandemic, 632 (6.0 %) were lost to follow-up, with characteristics of those lost to follow-up differing between IA types. 1444 individuals had at least one virtual visit followed within 30 days by an in-person follow-up. This was less common in males (OR 0.69-0.79) and more common for those with a higher frequency of physician visits prior to the pandemic (OR 1.27-1.32). CONCLUSION: Impacts of patterns of care during the pandemic should be further explored for healthcare planning to uphold optimal care access and promote effective use of virtual care.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 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".