Core Domain Set Selection According to OMERACT Filter 2.1: The OMERACT Methodology
Bibliographic record
Abstract
OBJECTIVE: To describe the Outcome Measures in Rheumatology (OMERACT) Filter 2.1 methodology for core domain set selection. METHODS: The "OMERACT Way for Core Domain Set selection" framework consists of 3 stages: first, generating candidate domains through literature reviews and qualitative work, then a process of consensus to obtain agreement from those involved, and finally formal voting on the OMERACT Onion. The OMERACT Onion describes the placement of domains in layers/circles: mandatory in all trials/mandatory in specific circumstances (inner circle); important but optional (middle circle); or research agenda (outer circle). Five OMERACT working groups presented their core domain sets for endorsement by the OMERACT community. Tools including a workbook and whiteboard video were created to assist the process. The methods workshop at OMERACT 2018 introduced participants to this framework. RESULTS: The 5 OMERACT working groups achieved consensus on their proposed core domain sets. After the Methodology Workshop training exercise at OMERACT 2018, over 90% of participants voted that they were confident that they understood the process of core domain set selection. CONCLUSION: The methods described in this paper were successfully used by the 5 working groups voting on domains at the OMERACT 2018 meeting, demonstrating the feasibility of the process. In addition, participants at OMERACT 2018 expressed increased confidence and understanding of the core domain set selection process after the training exercise. This methodology will continue to evolve, and we will use innovative technology such as whiteboard videos as a key part of our dissemination and implementation strategy for new methods.
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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 | 0.205 | 0.275 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.013 | 0.007 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".