Successful Stepwise Development of Patient Research Partnership: 14 Years’ Experience of Actions and Consequences in Outcome Measures in Rheumatology (OMERACT)
Bibliographic record
Abstract
There is increasing interest in making patient participation an integral component of medical research. However, practical guidance on optimizing this engagement in healthcare is scarce. Since 2002, patient involvement has been one of the key features of the Outcome Measures in Rheumatology (OMERACT) international consensus effort. Based on a review of cumulative data from qualitative studies and internal surveys among OMERACT participants, we explored the potential benefits and challenges of involving patient research partners in conferences and working group activities. We supplemented our review with personal experiences and reflections regarding patient participation in the OMERACT process. We found that between 2002 and 2016, 67 patients have attended OMERACT conferences, of whom 28 had sustained involvement; many other patients contributed to OMERACT working groups. Their participation provided face validity to the OMERACT process and expanded the research agenda. Essential facilitators have been the financial commitment to guarantee sustainable involvement of patients at these conferences, procedures for recruitment, selection and support, and dedicated time allocated in the program for patient issues. Current challenges include the representativeness of the patient panel, risk of pseudo-professionalization, and disparity in patients' and researchers' perception of involvement. In conclusion, OMERACT has embedded long-term patient involvement in the consensus-building process on the measurement of core health outcomes. This integrative process continues to evolve iteratively. We believe that the practical points raised here can improve participatory research implementation.
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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 | 0.159 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.003 | 0.029 |
| Research integrity | 0.004 | 0.008 |
| 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; 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".