Instrument Selection Using the OMERACT Filter 2.1: The OMERACT Methodology
Bibliographic record
Abstract
OBJECTIVE: Outcome Measures in Rheumatology (OMERACT) Filter 2.1 revised the process used for core outcome measurement set selection to add rigor and transparency in decision making. This paper describes OMERACT's methodology for instrument selection. METHODS: We presented instrument selection processes, tools, and reporting templates at OMERACT 2018, introducing the concept of "3 pillars, 4 questions, 7 measurement properties, 1 answer." Truth, discrimination, and feasibility are the 3 original OMERACT pillars. Based on these, we developed 4 signaling questions. We introduced the Summary of Measurement Properties table that summarizes the 7 measurement properties: truth (domain match, construct validity), discrimination [test-retest reliability, longitudinal construct validity (responsiveness), clinical trial discrimination, thresholds of meaning], and feasibility. These properties address a set of standards which, when met, answer the one question: Is there enough evidence to support the use of this instrument in clinical research of the benefits and harms of treatments in the population and study setting described? The OMERACT Filter 2.1 was piloted on 2 instruments by the Psoriatic Arthritis Working Group. RESULTS: The methodology was reviewed in a full plenary session and facilitated breakout groups. Tools to facilitate retention of the process (i.e., "The OMERACT Way") were provided. The 2 instruments were presented, and the recommendation of the working group was endorsed in the first OMERACT Filter 2.1 Instrument Selection votes. CONCLUSION: Instrument selection using OMERACT Filter 2.1 is feasible and is now being implemented.
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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 | 0.371 | 0.561 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.029 | 0.008 |
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".