Longitudinal changes in patient characteristics as drivers of clinical outcomes in the Early Undifferentiated PolyArthritis (EUPA) cohort
Bibliographic record
Abstract
OBJECTIVE: We investigated whether gradual clinical changes in RA patients diagnosed over two decades contributed to their improving outcomes during follow-up. METHODS: A total of 840 consecutive early RA patients were recruited in the Early Undifferentiated PolyArthritis (EUPA) cohort and assessed up to 5 years. Recruitment was categorized into three periods: (1) preBIO (1998-2004), (2) INNOV (2005-2010) and (3) T2Tp (2011-2022). Inverse probability of treatment weighting (IPTW) was used to account for baseline variations over time periods. Multivariate generalized estimating equations (GEEs) with repeated measures identified predictors of outcomes. RESULTS: The recruitment periods comprised 245, 266 and 329 recruited patients, respectively. After IPTW, baseline characteristics became largely similar across periods. Use of high-dose MTX and biologics markedly increased after 2005, remaining similar (MTX) or increasing slightly (biologics) in T2Tp relative to INNOV periods. Corticosteroid tapering accelerated and became more complete in the T2Tp period. In multivariable analyses, after balancing baseline characteristics and accounting for changes in diagnostic and therapeutic strategies over time, recruitment after 2011 remained strongly associated with faster and more prevalent ACR/EULAR remission but not with erosive status over follow-up. Despite improved clinical outcomes, improvement curves over 5 years for functional status and other patient-reported outcomes (PROs) remained unchanged across the three periods. CONCLUSIONS: Compared with the INNOV period, patients recruited after 2011 presented with milder baseline characteristics and showed evidence for an amplified response to treatment during follow-up, contributing to higher remission rates. Nonetheless, improvements of function and other PROs remained similar across recruitment periods.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".