Adherence to Treat-to-target Management in Rheumatoid Arthritis and Associated Factors: Data from the International RA BIODAM Cohort
Bibliographic record
Abstract
OBJECTIVE: Compelling evidence supports a treat-to-target (T2T) strategy for optimal outcomes in rheumatoid arthritis (RA). There is limited knowledge regarding the factors that impede implementation of T2T, particularly in a setting where adherence to T2T is protocol-specified. We aimed to assess clinical factors that associate with failure to adhere to T2T. METHODS: Patients with RA from 10 countries who were starting or changing conventional synthetic disease-modifying antirheumatic drugs and/or starting tumor necrosis factor inhibitors were followed for 2 years. Participating physicians were required per protocol to adhere to the T2T strategy. Factors influencing adherence to T2T low disease activity (T2T-LDA; 44-joint count Disease Activity Score ≤ 2.4) were analyzed in 2 types of binomial generalized estimating equations models: (1) including only baseline features (baseline model); and (2) modeling variables that inherently vary over time as such (longitudinal model). RESULTS: A total of 571 patients were recruited and 439 (76.9%) completed 2-year followup. Failure of adherence to T2T-LDA was noted in 1765 visits (40.5%). In the baseline multivariable model, a high number of comorbidities (OR 1.10, 95% CI 1.02-1.19), smoking (OR 1.32, 95% CI 1.08-1.63) and high number of tender joints (OR 1.03, 95% CI 1.02-1.04) were independently associated with failure to implement T2T, while anticitrullinated protein antibody/rheumatoid factor positivity (OR 0.63, 95% CI 0.50-0.80) was a significant facilitator of T2T. Results were similar in the longitudinal model. CONCLUSION: Lack of adherence to T2T in the RA BIODAM cohort was evident in a substantial proportion despite being a protocol requirement, and this could be predicted by clinical features. [Rheumatoid Arthritis (RA) BIODAM cohort; ClinicalTrials.gov: NCT01476956].
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".