The pattern of anti-IL-6 versus non-anti-IL-6 biologic disease modifying anti-rheumatic drugs use in patients with rheumatoid arthritis in Wales, UK: a real-world study using electronic health records
Bibliographic record
Abstract
Objective: non-anti-IL-6 (anti-TNF, B or T cell therapies) bDMARDs for RA. Methods: A retrospective cohort study of patients with the diagnosis of RA in the Secure Anonymised Information Linkage Databank, comprising primary and secondary care and specialist rheumatology clinic records for >90% of the population in Wales, UK. Patients initiated on first bDMARD treatment, discontinuation and clinical outcomes including infection and hospitalisation were analysed using Cox regression analysis. Results: Of patients identified with RA in their primary care records, 95.7% (4691/4922) received conventional synthetic DMARDs (csDMARDs). More than one-third (36.2%) were treated with bDMARDs (1784/4922). Of these biologic-naïve patients, 6.5% (116) were treated with anti-IL-6 bDMARDs; this treatment was associated with a previous history of infection [difference 8.8% (95% CI 1.1, 17.8)] and kidney disease [14.3% (95% CI 8.0, 22.5)]. Treatment discontinuation was significantly higher in the non-anti-IL-6 bDMARD-treated patients (23.1%) compared with the anti-IL-6 bDMARD-treated individuals (18.1%) [difference 9.4% (95% CI 1.1, 15.7)]. For those discontinuing a first line of treatment, 385 patients (23%) and 21 patients (18%) switched to an alternative bDMARD from the non-anti-IL-6 and anti-IL-6 groups, respectively. Conclusion: Comorbidities, history of infection and kidney disease were associated with choosing anti-IL-6 bDMARDs in biologic-naïve RA patients in Wales. Anti-IL-6 bDMARD-treated biologic-naïve patients were more likely to continue treatment than non-IL-6 bDMARD-treated patients.
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 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| 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".