Critical Outcomes in Longitudinal Observational Studies and Registries in Patients with Rheumatoid Arthritis: An OMERACT Special Interest Group Report
Bibliographic record
Abstract
OBJECTIVE: Outcomes important to patients are those that are relevant to their well-being, including quality of life, morbid endpoints, and death. These outcomes often occur over the longterm and can be identified in prospective longitudinal observational studies (PLOS). There are no standards for which outcome domains should be considered. Our overarching goal is to identify critical longterm outcome domains for patients with rheumatic diseases, and to develop a conceptual framework to measure and classify them within the scope of OMERACT Filter 2.0. METHODS: The steps of this initiative primarily concern rheumatoid arthritis (RA) and include (1) performing a systematic review of RA patient registries and cohorts to identify previously collected and reported outcome domains and measurement instruments; (2) developing a conceptual framework and taxonomy for identification and classification of outcome domains; (3) conducting focus groups to identify domains considered critical by patients with RA; and (4) surveying patients, providers, and researchers to identify critical outcomes that can be evaluated through the OMERACT filter. RESULTS: In our initial evaluation of databases and registries across countries, we found both commonalities and differences, with no clear standardization. At the initial group meeting, participants agreed that additional work is needed to identify which critical outcomes should be collected in PLOS, and suggested several: death, independence, and participation, among others. An operational strategy for the next 2 years was proposed. CONCLUSION: Participants endorsed the need for an initiative to identify and evaluate critical outcome domains and measurement instruments for data collection in PLOS.
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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 | 0.492 | 0.603 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".