Longterm Drug Survival of Tumor Necrosis Factor Inhibitors in Patients with Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: To evaluate longterm drug survival (proportion of patients still receiving treatment) and discontinuation of etanercept (ETN), infliximab (IFX), adalimumab (ADA), certolizumab pegol (CZP), and golimumab (GOL) using observational data from patients with rheumatoid arthritis (RA). METHODS: Following a systematic literature review, drug survival at 12 and 12-24 months of followup was estimated by summing proportions of patients continuing treatment and dividing by number of studies. Drug survival at ≥ 36 months of followup was estimated through Metaprop. RESULTS: There were 170 publications included. In the first-line setting, drug survival at 12 months with ETN, IFX, or ADA was 71%, 69%, and 70%, respectively, while at 12-24 months the corresponding rates were 63%, 57%, and 59%. In the second-line setting, drug survival at 12 months with ETN, IFX, or ADA was 61%, 69%, and 55%, respectively, while at 12-24 months the corresponding rates were 53%, 39%, and 43%. Drug survival at ≥ 36 months with ETN, IFX, or ADA in the first-line setting was 59% (95% CI 46-72%), 49% (95% CI 43-54%), and 51% (95% CI 41-60%), respectively, while in the second-line setting the corresponding rates were 56% (95% CI 52-61%), 48% (95% CI 40-55%), and 41% (95% CI 36-47%). Discontinuation of ETN, IFX, and ADA at 36 months of followup was 38-48%, 42-62%, and 38-59%, respectively. Data on CZP and GOL were scarce. CONCLUSION: After > 12 months of followup, more patients with RA receiving ETN remain on treatment compared with other tumor necrosis factor inhibitors.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.001 | 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".