Unravelling the Cost of Biological Strategies in Rheumatoid Arthritis: A Kaleidoscope of Methodologies, Interpretations, and Interests
Bibliographic record
Abstract
In this issue of The Journal of Rheumatology , Müskens, et al describe the effect of the introduction of an etanercept (ETN) biosimilar on antirheumatic medication cost1. After a Dutch rheumatology department launched this biosimilar as a substitute for the more expensive biologic ETN, the accumulated 3-monthly antirheumatic medication cost in that hospital pertaining to in- and outpatients with rheumatoid arthritis (RA), mainly consisting of cost of biologic disease-modifying antirheumatic drugs (bDMARD), decreased, as expected. However, this financial advantage was lost within less than a year, due to an increase of the percentage of the patients with RA treated with a bDMARD. This means that the potential savings of using the biosimilar were spent on extra patients treated with a bDMARD, although the rheumatologists had not formally changed their bDMARD prescription policy. The brisk increase in percentage of patients treated with a bDMARD in this time period is not compatible with the general trend of slowly increasing bDMARD use over time. Should the reader of the paper1 thus conclude that introduction of cheaper biosimilars is not effective in reducing medication cost in the longer term? Our answer would be that interpretations of this, and of any costing study, strongly depend on what we are looking at, how we are looking, and who is looking. What we are looking at: Treatment strategy Müskens, et al 1 found no statistically significant difference in disease activity in those starting a biological before the biosimilar introduction (mean Disease Activity Score assessing 28 joints [DAS28] 4.7), versus in those starting a bDMARD after the biosimilar introduction (DAS28 4.5). Notably, the mean age of patients at the start of bDMARD after the biosimilar introduction was statistically significantly higher than that before the biosimilar introduction (58 vs 52 yrs, respectively). After the biosimilar introduction, … Address correspondence to Dr. J.W. Jacobs, Department of Rheumatology & Clinical Immunology, G02.228, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, the Netherlands. Email: j.w.g.jacobs-12@umcutrecht.nl.
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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 | 0.097 | 0.218 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.003 | 0.023 |
| Scholarly communication | 0.016 | 0.037 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.009 | 0.027 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".