Longterm Retention Rate and Risk Factor for Discontinuation Due to Insufficient Efficacy and Adverse Events in Japanese Patients with Rheumatoid Arthritis Receiving Etanercept Therapy
Bibliographic record
Abstract
OBJECTIVE: Assessing retention rate and risk factor for drug discontinuation is important for drug evaluation. We examined a 3-year retention rate and the risk factor for discontinuation due to insufficient efficacy (IE) and adverse events (AE) in Japanese patients with rheumatoid arthritis (RA) who are receiving etanercept (ETN). METHODS: Data were collected from 588 patients treated with ETN as a first biologic from the Tsurumai Biologics Communication Registry. Baseline characteristics for the incidence of both IE and AE were analyzed using the Cox proportional-hazards regression model. Patients were divided into groups based on age and concomitant methotrexate (MTX). Drug retention rates were calculated using the Kaplan-Meier method and compared among groups using the log-rank test. RESULTS: ETN monotherapy without concomitant MTX [MTX(-)] was significantly related to a higher incidence of discontinuation due to IE [hazard ratio (HR) = 2.226, 95% CI 1.363-3.634]. Older age and MTX(-) were significantly related to a higher incidence of discontinuation due to AE [HR = 1.040, 1.746, 95% CI 1.020-1.060, 1.103-2.763, respectively]. The MTX(-)/≥ 65 years group had the lowest retention rate (p < 0.001). The discontinuation rate due to IE was lower in the MTX(+)/< 65 years group compared to < 65 years/MTX(-), ≥ 65 years/MTX(-) group (p = 0.006, p < 0.001, respectively). The discontinuation rate due to AE was highest in the MTX(-)/≥ 65 years group (p < 0.001). CONCLUSION: Our findings suggest that the risk of discontinuation due to IE was high in the patients who did not use concomitant MTX and that the risk of discontinuation due to AE was high in elderly patients who did not use concomitant MTX.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".