Mirror, Mirror, on the Wall, Which Is the Most Effective Biologic of All?
Bibliographic record
Abstract
We all consider the “comparative effectiveness” of available treatment options in daily clinical practice, knowingly or unknowingly. In fact, that and “comparative safety” are 2 of our most important and constant considerations when planning the best possible therapy for any disorder in rheumatology. In daily practice, clinicians mostly depend on their personal experience and knowledge, because head-to-head randomized controlled trials (RCT) are not available for most of the medications we use, and for most of the rheumatologic conditions we treat. Introduction of biologic therapy to treat immune-mediated inflammatory conditions has made this task even more challenging, because these agents have proven to be generally very effective but potentially toxic, and they are uniformly expensive. Which biologic should we choose to treat a patient with active rheumatoid arthritis, psoriatic arthritis, or ankylosing spondylitis (AS)? Several times, the economic realities of health insurance coverage tie our hands and choose the biologic therapy for us, and in the absence of scientific data on comparative effectiveness or safety, it is difficult to argue one way or the other. Treatment guidelines developed by national or international societies play a big role in helping us choose the right treatment, and comparative effectiveness is considered formally with statistical analysis or informally with expert opinion when developing such guidelines. Several statistical methods are available for assessing comparative effectiveness. Treatment guidelines by the American College of Rheumatology use network metaanalysis (NMA), whereas the UK National Institute for Health and Care … Address correspondence to Dr. A. Deodhar, Professor of Medicine, Division of Arthritis & Rheumatic Diseases (OP09), Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, Oregon 97239, USA. E-mail: deodhara{at}ohsu.edu
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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 | 0.018 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.008 | 0.019 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.022 | 0.011 |
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".