Updating the OMERACT Filter: Implications of Filter 2.0 to Select Outcome Instruments Through Assessment of “Truth”: Content, Face, and Construct Validity
Bibliographic record
Abstract
OBJECTIVE: The Outcome Measures in Rheumatology (OMERACT) Filter provides guidelines for the development and validation of outcome measures for use in clinical research. The "Truth" section of the OMERACT Filter requires that criteria be met to demonstrate that the outcome instrument meets the criteria for content, face, and construct validity. METHODS: Discussion groups critically reviewed a variety of ways in which case studies of current OMERACT Working Groups complied with the Truth component of the Filter and what issues remained to be resolved. RESULTS: The case studies showed that there is broad agreement on criteria for meeting the Truth criteria through demonstration of content, face, and construct validity; however, several issues were identified that the Filter Working Group will need to address. CONCLUSION: These issues will require resolution to reach consensus on how Truth will be assessed for the proposed Filter 2.0 framework, for instruments to be endorsed by OMERACT.
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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 | 0.696 | 0.789 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.010 | 0.010 |
| Research integrity | 0.014 | 0.018 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".