Updating the OMERACT Filter: Implications for Patient-reported Outcomes
Bibliographic record
Abstract
OBJECTIVE: At a previous Outcome Measures in Rheumatology (OMERACT) meeting, participants reflected on the underlying methods of patient-reported outcome (PRO) instrument development. The participants requested proposals for more explicit instrument development protocols that would contribute to an enhanced version of the "Truth" statement in the OMERACT Filter, a widely used guide for outcome validation. In the present OMERACT session, we explored to what extent these new Filter 2.0 proposals were practicable, feasible, and already being applied. METHODS: Following overview presentations, discussion groups critically reviewed the extent to which case studies of current OMERACT Working Groups complied with or negated the proposed PRO development framework, whether these observations had a more general application, and what issues remained to be resolved. RESULTS: Several aspects of PRO development were recognized as particularly important, and the need to directly involve patients at every stage of an iterative PRO development program was endorsed. This included recognition that patients contribute as partners in the research and not merely as subjects. Correct communication of concepts with the words used in questionnaires was central to their performance as measuring instruments, and ensuring this understanding crossed cultural and linguistic boundaries was important in international studies or comparisons. CONCLUSION: Participants recognized, endorsed, and were generally already putting into practice the principles of PRO development presented in the plenary session. Further work is needed on some existing instruments and on establishing widespread good practice for working in close collaboration with patients.
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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 | 0.669 | 0.791 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".