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 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 | 0.017 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".