Oral glucocorticoid use in patients with rheumatoid arthritis initiating TNF-inhibitors, tocilizumab or abatacept: Results from the international TOCERRA and PANABA observational collaborative studies
Bibliographic record
Abstract
OBJECTIVE: To evaluate and compare the use of oral glucocorticoids with three classes of bDMARDs in patients with rheumatoid arthritis (RA). METHODS: We included patients from 13 observational registries treated with a TNF-inhibitor, abatacept or tocilizumab and with available information on the use of oral glucocorticoids. The main outcome was oral glucocorticoid withdrawal. A McNemar test was used to analyse the change in the use of glucocorticoids after 1 year. Kaplan-Meier estimates and Cox regressions, adjusted for patient, treatment, and disease characteristics, were used to evaluate glucocorticoid discontinuation in patients with glucocorticoids at baseline. Because of heterogeneity, analyses were done by registers and pooled using random-effects meta-analysis. RESULTS: A total of 12,334 participants treated with TNF-inhibitors, 2100 with tocilizumab and 3229 with abatacept were included. At one-year, oral glucocorticoid use decreased in all treatment groups (odds ratio for stopping vs. starting of 2.19 [95% CI 1.58; 3.04] for TNF-inhibitors, 2.46 [1.39; 4.35] for tocilizumab; 1.73 [1.25; 2.21] for abatacept). Median time to glucocorticoid withdrawal was ≈2 years or more in most countries, with a gradual decrease over time. Compared to TNF-inhibitors, crude hazard ratios of glucocorticoid discontinuation were 0.65[0.48-0.87] for abatacept, and 1.04 [0.76-1.43] for tocilizumab, and adjusted hazard ratios were 1.1 [0.83-1.47] for abatacept, and 1.30 [0.96-1.78] for tocilizumab. CONCLUSION: After initiation of a bDMARD, glucocorticoid use decreased similarly in all treatment groups. However, glucocorticoid withdrawal was much slower than advocated by current international guidelines. More effort should be devoted to glucocorticoid tapering when low disease activity is achieved.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".