Proposal for Levels of Evidence Schema for Validation of a Soluble Biomarker Reflecting Damage Endpoints in Rheumatoid Arthritis, Psoriatic Arthritis, and Ankylosing Spondylitis, and Recommendations for Study Design
Bibliographic record
Abstract
OBJECTIVE: At OMERACT 8 a framework for levels of evidence was proposed for the validation of biomarkers as surrogate outcome measures. We aimed to adapt this scheme in order to apply it in the setting of soluble biomarkers proposed to replace the measurement of damage endpoints in rheumatoid arthritis (RA), psoriatic arthritis (PsA), and ankylosing spondylitis (AS). We also aimed to generate consensus on minimum standards for the design of longitudinal studies aimed at validating biomarkers. METHODS: Before the meeting, the Soluble Biomarker Working Group prepared a preliminary framework and discussed various models for association and prediction related to the statistical strength domain. In addition, 3 Delphi exercises addressing longitudinal study design for RA, PsA, and AS were conducted within the working group and members of the Assessments in SpondyloArthritis International Society (ASAS) and the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA). This formed the basis for discussions among OMERACT 9 participants. RESULTS: The proposed framework was accepted by consensus. In the study design domain a requirement for both prospective observational studies and randomized controlled trials (RCT) in different drug classes was noted. A template for determining the level of statistical strength was proposed. The addition of a new domain on biomarker assay performance was considered essential, and participants suggested that for any biomarker this domain should be addressed first, i.e., before starting clinical validation studies. Participants agreed on most elements of a longitudinal study design template. Where consensus was lacking the working group has drafted solutions that constitute a basis for prospective validation studies. CONCLUSION: The OMERACT 9 Soluble Biomarker Group has successfully formulated a levels of evidence scheme and a study design template that will provide guidance to conduct validation studies in the setting of soluble biomarkers proposed to replace the measurement of damage endpoints in RA, PsA, and AS.
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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 | 0.578 | 0.568 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.010 | 0.030 |
| Bibliometrics | 0.026 | 0.012 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.024 | 0.021 |
| Open science | 0.022 | 0.020 |
| Research integrity | 0.038 | 0.045 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".