Updating the OMERACT Filter: Implications for Imaging and Soluble Biomarkers
Bibliographic record
Abstract
OBJECTIVE: The Outcome Measures in Rheumatology (OMERACT) Filter provides a framework for the validation of outcome measures for use in rheumatology clinical research. However, imaging and biochemical measures may face additional validation challenges because of their technical nature. The Imaging and Soluble Biomarker Session at OMERACT 11 aimed to provide a guide for the iterative development of an imaging or biochemical measurement instrument so it can be used in therapeutic assessment. METHODS: A hierarchical structure was proposed, reflecting 3 dimensions needed for validating an imaging or biochemical measurement instrument: outcome domain(s), study setting, and performance of the instrument. Movement along the axes in any dimension reflects increasing validation. For a given test instrument, the 3-axis structure assesses the extent to which the instrument is a validated measure for the chosen domain, whether it assesses a patient-centered or disease-centered variable, and whether its technical performance is adequate in the context of its application. Some currently used imaging and soluble biomarkers for rheumatoid arthritis, spondyloarthritis, and knee osteoarthritis were then evaluated using the original OMERACT Filter and the newly proposed structure. Breakout groups critically reviewed the extent to which the candidate biomarkers complied with the proposed stepwise approach, as a way of examining the utility of the proposed 3-dimensional structure. RESULTS: Although there was a broad acceptance of the value of the proposed structure in general, some areas for improvement were suggested including clarification of criteria for achieving a certain level of validation and how to deal with extension of the structure to areas beyond clinical trials. CONCLUSION: General support was obtained for a proposed tri-axis structure to assess validation of imaging and soluble biomarkers; nevertheless, additional work is required to better evaluate its place within the OMERACT Filter 2.0.
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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 | 0.525 | 0.766 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.007 | 0.010 |
| 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".