Biologics initiation in moderate versus severe rheumatoid arthritis: real-world experience from a Canadian registry
Bibliographic record
Abstract
OBJECTIVES: To evaluate the treat-to-target experience, and quality of life measures of moderate and severe rheumatoid arthritis (RA) patients initiating a biologic in a real-world setting of a publicly funded payer system. METHODS: Biologic naive RA patients who had initiated their first biologic while enrolled in the Ontario Best Practices Research Initiative registry from 2008 to 2020 were selected if they had moderate (DAS28 >3.2 to ≤5.1) or severe (DAS28 >5.1) RA. Remission, LDA, DAS28, HAQ-DI, fatigue, sleep, drug persistence and characteristics associated with remission were assessed at 12 months post biologic initiation. RESULTS: Overall, 838 patients initiated their first biologic, 264 had moderate RA and 219 had severe RA. After 12 months, 44% moderate RA vs. 21% severe RA achieved remission (p<0.0001), and 59% moderate RA vs. 35% severe RA reached LDA (p<0.0001). Mean change (SD) from baseline in DAS28 was 2.2 (1.5) in severe RA vs. 1.4 (1.3) in moderate RA (p<0.0001), in fatigue score was 1.11 (3.2) in severe RA vs. 0.98 (3.2) in moderate RA (p<0.0001). Moderate disease at a biologic initiation was positively associated with remission (p=0.0016). Female gender (p=0.0170), and a higher HAQ-DI score at baseline (p=0.0042) were negatively associated with remission. Biologic persistence was 77% for moderate, and 73% for severe (p=0.2444). CONCLUSIONS: Severe RA patients had higher mean score improvements in DAS28, sleep and fatigue. Moderate RA was more likely to reach remission or LDA. Both groups had similar biologic persistence at 12 months. These findings highlight the importance of the treat-to-target approach and its potential underutilisation in the real-world setting.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| 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".