Adalimumab and Etanercept in the Treatment of Rheumatoid Arthritis and Spondyloarthropathies: Budget Impact Model of Dose Reduction
Bibliographic record
Abstract
Objective:To assess the financial impact ofspacing out the administration intervals of adalimumab (ADA) and etanercept (ETN) in the treatment of rheumatoid arthritis (RA) and spondyloarthropathies (SAP) in our work setting.Materials and method:A budget impact model (BIM) was developed to estimate the financial impact ofspacing out the usual administration intervals of ADA 40 mg every 2 weeks and ETN 50 mg weekly (scenario A) to ADA 40 mg every 3 weeks and ETN 50 mg every 2 weeks (scenario B), according to the guidelines and recommendations applied to these studies, specifying the target population, the study perspective, the time frame, and analysing the robustness of the study with a threshold univariate sensitivity analysis.Results:A total of 71 patients were included in the study.The application of a BIM showed annual savings for ADA and ETN of €19,784 and €38,271, respectively.The net cost, that is, the savings this entailed for the time frame considered (2 years), amounted to €116,110.The sensitivity analysis performed shows that the BIM estimated for the study period was very robust, as the net result in the different scenarios varied very little, remaining negative in the new scenarios.Conclusions:The BIM developed in the study shows the importance of the role of healthcare professionals in the context of sustainability of the healthcare system, where the model could generate large annual net savings for the different regional healthcare systems.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".