Timely escalation to second-line therapies after failure of methotrexate in patients with early rheumatoid arthritis does not reduce the risk of becoming difficult-to-treat
Bibliographic record
Abstract
BACKGROUND: To investigate the frequency of difficult-to-treat (D2T) rheumatoid arthritis (RA) in patients early escalated to biologic/targeted synthetic disease modifying anti-rheumatic drugs (b/tsDMARDs) after failure of treat-to-target with methotrexate (MTX). METHODS: From a prospective cohort of early RA, all patients with their first access in the years 2005-2018, and eventually starting a b/tsDMARD before the end of 2022, were included and followed-up until April 2024. Study outcomes included drug survival on each consecutive b/tsDMARDs, development of D2T (according to the EULAR definition and subsequent modifications), and its predictors. RESULTS: Of a total cohort of 722 early RA patients treated with initial MTX and followed-up for at least 3 years from diagnosis, 155 (21.5%) had started a b/tsDMARD after a median of 19 months. In more than 70% of the cases, RA was uncontrolled despite optimal doses of MTX of ≥ 15 mg/day. The retention rates of the first and the second b/tsDMARD were approximatively 70% after 1 year but dropped to 40% after 5 years. After a median (IQR) follow up of 72.6 (34.5-134.2) months, 45 patients (29%) fulfilled the EULAR D2T criteria. At multivariable analysis, higher number of swollen joints and worse pain scores were confirmed as predictors of D2T. Furthermore, in this early RA cohort, shorter disease duration at the start of treatment with b/tsDMARDs, together with negativity for autoantibodies, were also independent predictors of D2T. CONCLUSIONS: Early implementation of treatment after failure of treat-to-target with MTX may not prevent the development of D2T in RA. Patients showing early refractoriness to conventional drugs and those lacking autoantibodies are at higher risk of multiple treatment failures.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".