Health Equity Considerations for Developing and Reporting Patient-reported Outcomes in Clinical Trials: A Report from the OMERACT Equity Special Interest Group
Bibliographic record
Abstract
OBJECTIVE: Despite advances integrating patient-centered outcomes into rheumatologic studies, concerns remain regarding their representativeness across diverse patient groups and how this affects equity. The Outcome Measures in Rheumatology (OMERACT) Equity Working Group aims to determine whether and how to address equity issues within the core outcome sets of domains and instruments. METHODS: We surveyed current and previous OMERACT meeting attendees and members of the Campbell and Cochrane Equity Group regarding whether to address equity issues within the OMERACT Filter 2.0 Core Outcome Sets and how to assess the appropriateness of domains, instruments, and measurement properties among diverse patients. At OMERACT 2016, results of the survey and a narrative review of differential psychosocial effects of rheumatoid arthritis (i.e., on men) were presented to stimulate discussion and develop a research agenda. RESULTS: We proposed 6 moments for which an equity lens could be added to the development, selection, or testing of patient-reported outcome measures (PROM): (1) recruitment, (2) domain selection, (3) feasibility in diverse settings, (4) instrument validity, (5) thresholds of meaning, and (6) consideration of statistical power of subgroup analyses for outcome reporting. CONCLUSION: There is a need to (1) conduct a systematic review to assess how equity and population characteristics have been considered in PROM development and whether these differences influence the ranking of importance of outcome domains or a patient's response to questionnaire items, and (2) conduct the same survey described above with patients representing groups experiencing health inequities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.725 | 0.809 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.015 | 0.021 |
| Insufficient payload (model declined to judge) | 0.003 | 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".