Medication Persistence of Disease‐Modifying Antirheumatic Drugs and Anti–Tumor Necrosis Factor Agents in a Cohort of Patients With Rheumatoid Arthritis in Brazil
Bibliographic record
Abstract
OBJECTIVE: To assess the use and persistence of anti-tumor necrosis factor (anti-TNF) versus disease-modifying antirheumatic drug (DMARD) therapies in patients with rheumatoid arthritis (RA) in Brazil. METHODS: This was a new-user cohort study of RA patients from 2003 to 2010, using administrative data. Individuals were classified as being persistent using a drug at the first year and the first 2 years after cohort entry, if they did not discontinue that drug during that period. Cox regression was used to identify potential determinants of discontinuation of therapy in each medication group. RESULTS: Among 76,351 patients, 14,313 were using anti-TNF (+/- DMARD) therapy. At the end of the first year of followup, 48.2% continued using anti-TNF (+/- DMARD) therapy compared to 42.6% who persisted with DMARDs only. At the end of the second year, 23.1% of anti-TNF (+/- DMARD) users and 19.3% of DMARD-only users continued with therapy. Infliximab users had the lowest persistence rates. Multivariate Cox regression analysis showed that among anti-TNF (+/- DMARD) users, higher discontinuation rates were observed in female patients, in patients with lower income (only at the first 2 years of followup), in nonresidents of the region with the highest Human Development Index (HDI) rates, in those with a higher comorbidity score, and in those enrolled in the 2003-2006 period. Among DMARD-only users, younger patients, patients with lower income, nonresidents in regions with high HDI, those with a higher comorbidity score, and those enrolled in the 2003-2006 period were also more likely to discontinue therapy. CONCLUSION: Brazilian patients with RA showed low rates of medication persistence for DMARDs and anti-TNF agents, particularly at the first 2 years of followup. Future work could determine what other factors might contribute to drug persistence in RA.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".