Treating to a Target in Established Active Rheumatoid Arthritis Patients Receiving a Tumor Necrosis Factor Inhibitor: Results From a Real‐World Cluster‐Randomized Adalimumab Trial
Bibliographic record
Abstract
OBJECTIVE: In early rheumatoid arthritis (RA), treating to a target is more effective than routine care (RC). Our aim was to determine if treating to a target has better outcomes than RC in established active RA. METHODS: We used a real-world, 18-month cluster-randomized trial in established active RA patients treated with adalimumab. Physicians were randomized to RC, treating to a Disease Activity Score in 28 joints (DAS28) of <2.6 (DAS group), or treating to a 0 of 28 swollen joint count (SJC; 0-SJC group). RESULTS: Among the 308 enrolled patients, 109 (35.4%) were randomized to RC, 100 (32.5%) to the DAS group, and 99 (32.1%) to the 0-SJC group. When adjusting for baseline DAS28, a comparable but significant (P < 0.001) improvement in DAS28 was observed at 12 months for all groups (DAS28 mean score 3.1, 3.4, and 3.2, respectively). There were no significant between-group differences in the improvement of clinical parameters and patient-reported outcomes with the exception of the mean change in patient satisfaction over time (P = 0.020), which was highest in the DAS group. Time to achieving good/moderate European League Against Rheumatism (EULAR) response was significantly shorter in the targeted treatment groups compared to RC (adjusted hazard ratio [HR] for the DAS-group 2.99 [95% confidence interval (95% CI) 1.71-5.24] and HR for the 0-SJC group 1.86 [95% CI 1.09-3.13]). The dropout rate was 52.3% in RC, 27% in the DAS group, and 22.2% in the 0-SJC group (P < 0.001). CONCLUSION: All groups experienced significant improvements at 18 months of treatment with adalimumab. Treating to target in established RA did not differ from RC in terms of therapeutic end point achievement for patients remaining on treatment. However, time to achieving good/moderate EULAR response was significantly shorter in the targeted treatment groups compared to RC and, importantly, the dropout rate was significantly lower with targeted treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".