A Bridge Too Far? Real‐World Practice Patterns of Early Glucocorticoid Use in the Canadian Early Arthritis Cohort
Bibliographic record
Abstract
OBJECTIVE: To describe patterns of glucocorticoid use in a large real-world cohort with early rheumatoid arthritis (RA) and assess the impact on disease activity and treatment. METHODS: Data are from adults with new RA (≤1 year) recruited to the Canadian Early Arthritis Cohort (CATCH) and are stratified on the basis of whether a person was prescribed oral glucocorticoids within 3 months of study entry. Disease activity was compared over 24 months. Mixed-effects logistic regression was used for adjusted odds ratios (aORs) of escalation to biologics separately for 12 and 24 months, with random effects terms to account for prescribing patterns clustering by study site. RESULTS: Among 1891 persons, 30% received oral steroids. Users were older, were less often employed, and had shorter disease duration and higher disease activity. Disease activity improved over time, with early glucocorticoid users starting at higher levels of disease activity. Participants with early oral glucocorticoids were more likely to be on a biologic at 12 months (aOR = 2.4; 95% confidence interval [CI], 1.5-3.7) and 24 months (aOR = 1.9; 95% CI, 1.3-3.0). Despite Canadian clinical practice guidelines to limit corticosteroid use to short-term or 'bridge' therapy, 30% of patients who used oral glucocorticoids still used them 2 years later. CONCLUSION: Early steroids were prescribed sparingly in CATCH and were often indicative of more active baseline disease as well as the need for progression to biologics.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".