Effectiveness of a treat-to-target strategy in patients with moderate to severely active rheumatoid arthritis treated with abatacept
Bibliographic record
Abstract
BACKGROUND: To compare a treat-to-target (T2T) approach and routine care (RC) in adults with active to severely active rheumatoid arthritis (RA) initiating subcutaneous abatacept. METHODS: A 12-month cluster-randomized trial in active RA patients treated with abatacept was conducted. Physicians were randomized to RC or T2T with a primary endpoint of achieving sustained Clinical Disease Activity Index (CDAI) low disease activity (LDA) at two consecutive assessments approximately 3 months apart. Additional outcomes included Simple Disease Activity Index (SDAI), Disease Activity Score 28-CRP (DAS28-CRP), Routine Assessment of Patient Index Data 3 (RAPID3), and the Health Assessment Questionnaire-Disability Index (HAQ-DI). Time to achieve therapeutic endpoints was assessed with survival analysis. RESULTS: Among the 284 enrolled patients, 130 were in the T2T group and 154 in RC. Primary endpoint was achieved by 36.9% and 40.3% of patients in T2T and RC groups, respectively. No significant between-group differences were observed in the odds of achieving secondary outcomes, except for a higher likelihood of CDAI LDA in the T2T group vs. RC (odds ratio [95% confidence interval]: 1.33 [1.03-1.71], p = 0.0263). Compared with RC, patients in the T2T group achieved SDAI remission significantly faster (Kaplan-Meier-estimated mean [standard error]: 14.0 [0.6] vs. 19.3 [0.8] months, p = 0.0428) with a trend toward faster achievement of CDAI LDA/remission, DAS28-CRP remission, and HAQ-DI minimum clinically important difference. CONCLUSIONS: Patients managed per T2T and those under RC experienced significant improvements in RA disease activity at 12 months of abatacept treatment. T2T was associated with higher odds of CDAI LDA and a shorter time to achieving therapeutic endpoints. TRIAL REGISTRATION: Name of the registry: ClinicalTrials.gov. TRIAL REGISTRATIONS: NCT03274141 . Date of registration: September 6, 2017.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".