Impact of anti-rheumatic treatment on the individual components of the ACR composite score in patients with rheumatoid arthritis: real-world data
Bibliographic record
Abstract
OBJECTIVES: Standard criteria for measuring treatment efficacy in patients with rheumatoid arthritis (RA) include American College of Rheumatology (ACR) response rates, which require meeting a threshold of ≥20/50/70% improvement in several physician- and patient-reported measures. We aimed to evaluate the impact of csDMARDs, TNF inhibitors (TNFi), and tofacitinib (TOFA) on ACR components in real-life practice. METHODS: Clinical data of RA patients with a CDAI >10 at the time they started a treatment were pooled from two registries: Ontario Best Practices Research Initiative (OBRI) and RHUMADATA. Endpoints included proportions of patients achieving: ACR20/50/70 responses, ≥20/50/70% improvements and mean percentage improvement in individual ACR components at Month 6. We also adjusted for potential confounders to compare impact of these medications on outcomes of interest. RESULTS: A total of 669 patients were included (csDMARD, n=157, TNFi, n=252; TOFA, n=260). An overall higher proportion in all three-medication groups achieved ≥20/50/70% improvement in primary ACR components vs. secondary components. Among secondary components, ≥20/50/70% improvement rates were numerically highest for PhGA and lowest for HAQ-DI and pain. Among ACR20/50/70 responders for all medications, the mean percentage improvement was more than 80% for primary components, and ranged from 30% to 80% for secondary components. A significantly lower proportion of patients in TNFi group achieved to at least 50% improvement in pain compared to TOFA after adjusting. CONCLUSIONS: In this real-world practice, physician-reported measures contribute slightly more to overall ACR20/50/70 responses. Pain was the most important factor in achieving an ACR50 TOFA users, possibly reflecting the different effects of JAKi on pain.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".