<p>Persistence to subcutaneous biological agents in Hungarian patients treated for inflammatory arthritis</p>
Bibliographic record
Abstract
BACKGROUND: The aim of the study was to compare drug survival rate of subcutaneous tumor necrosis factor alpha inhibitors in rheumatoid arthritis, ankylosing spondylitis, and psoriatic arthritis patients in Hungary. METHODS: This was a retrospective analysis using data collected from 5,647 patients over a period of 10 years who were treated with any of the following drugs: adalimumab (ADA), etanercept, certolizumab pegol (CZP), and golimumab (GLM). National Health Insurance Fund's hospital, drug reimbursement, and special reimbursement registry data have been used in this study. Drug survival rate was calculated according to Kaplan-Meier survival analysis. Propensity score matching was used to reduce the potential bias caused by the inhomogeneity resulting from demographic characteristics, patient pathways, or drug administration protocols. Both raw and propensity matched data were subject of pairwise comparison between the four subcutaneous therapies. RESULTS: The overall rate of persistence for the 4 biological therapies was between 53% and 61% after 1 year and between 14% and 19% after 4 years (follow-up time). Pairwise comparisons between therapies showed significant differences with GLM-treated patients showing longer median survival times than patients on other therapies. After propensity matching, these differences remained statistically significant between GLM and ADA or CZP over 4 years. CONCLUSION: Hungarian show longer persistence to GLM compared to ADA and CZP.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".