Time to remission in swollen joints is far faster than patient reported outcomes in rheumatoid arthritis: results from the Ontario Best Practices Research Initiative (OBRI)
Bibliographic record
Abstract
OBJECTIVES: RA patients are often not in remission due to patient global assessment of disease activity (PtGA) included in disease activity indices. The aim was to assess the lag of patient-reported outcomes (PROs) after remission measured by clinical disease activity index (CDAI) or swollen joint count (SJC28). METHODS: RA patients enrolled in the Ontario Best Practices Research Initiative registry not in low disease state at baseline with at ≥6 months of follow-up, were included. Low disease state was defined as CDAI ≤ 10, SJC28 ≤ 2, PtGA ≤ 2cm, pain score ≤ 2cm, or fatigue ≤ 2cm. Remission included CDAI ≤ 2.8, SJC28 ≤ 1, PtGA ≤ 1cm, pain score ≤ 1cm, or fatigue ≤ 1cm. Time to first low disease state/remission based on each definition was calculated overall and stratified by early vs established RA. RESULTS: A total of 986 patients were included (age 57.4 (12.9), disease duration 8.3 (9.9) years, 80% women). The median (95% CI) time in months to CDAI ≤ 10 was 12.4 (11.4, 13.6), SJC28 ≤ 2 was 9 (8.2, 10), PtGA ≤ 2cm was 18.9 (16.1, 22), pain ≤ 2cm was 24.5 (19.4, 30.5), and fatigue ≤ 2cm was 30.4 (24.8, 31.7). For remission, the median (95% CI) time in months to CDAI ≤ 2.8 was 46.5 (42, 54.1), SJC28 ≤ 1 was 12.5 (11.4, 13.4), PtGA ≤ 1cm was 39.6 (34.6, 44.8), pain ≤ 1cm was 54.7 (43.6, 57.5) and fatigue ≤ 1cm was 42.6 (36.8, 48). Time to achieving low disease state and remission was generally significantly shorter in early RA compared with established RA with the exception of fatigue. CONCLUSION: Time to achieving low disease state or remission based on PROs was considerably longer compared with swollen joint count. Treating to a composite target in RA could lead to inappropriate changes in DMARDs.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".