Inconsistent Treatment with Disease-modifying Antirheumatic Drugs: A Longitudinal Data Analysis
Bibliographic record
Abstract
OBJECTIVE: Current recommendations advocate treatment with disease-modifying antirheumatic drugs (DMARD) in all patients with active rheumatoid arthritis (RA). We investigated the frequency of and reasons for inconsistent DMARD use among patients in a clinical rheumatology cohort. METHODS: Patients in the Brigham Rheumatoid Arthritis Sequential Study were studied for DMARD use (any or none) at each semiannual study timepoint during the first 2 study years. Inconsistent use was defined as DMARD use at ≤ 40% of study timepoints. Characteristics were compared between inconsistent and consistent users (> 40%), and factors associated with inconsistent DMARD use were determined through multivariate logistic regression. A medical record review was performed to determine the reasons for inconsistent use. RESULTS: Of 848 patients with ≥ 4 out of 5 visits recorded, 55 (6.5%) were inconsistent DMARD users. Higher age, longer disease duration, and rheumatoid factor negativity were statistically significant correlates of inconsistent use in the multivariate analyses. The primary reasons for inconsistent use identified through chart review, allowing for up to 2 co-primary reasons, were inactive disease (n = 28, 50.9%), intolerance to DMARD (n = 18, 32.7%), patient preference (n = 7, 12.7%), comorbidity (n = 6, 10.9%), DMARD not being effective (n = 3, 5.5%), and pregnancy (n = 3, 5.5%). During subsequent followup, 14/45 (31.1%) inconsistent users with sufficient data became consistent users of DMARD. CONCLUSION: A small proportion of patients with RA in a clinical rheumatology cohort were inconsistent DMARD users during the first 2 years of followup. While various patient factors correlate with inconsistent use, many patients re-start DMARD and become consistent users over time.
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".