Toward the Development of a Core Set of Outcome Domains to Assess Shared Decision-making Interventions in Rheumatology: Results from an OMERACT Delphi Survey and Consensus Meeting
Bibliographic record
Abstract
OBJECTIVE: The aim of this Outcome Measures in Rheumatology (OMERACT) Working Group was to determine the core set of outcome domains and subdomains for measuring the effectiveness of shared decision-making (SDM) interventions in rheumatology clinical trials. METHODS: Following the OMERACT Filter 2.0, and based on a previous literature review of SDM outcome domains and a nominal group process at OMERACT 2014, (1) an online Delphi survey was conducted to gather feedback on the draft core set and refine its domains and subdomains, and (2) a workshop was held at the OMERACT 2016 meeting to gain consensus on the draft core set. RESULTS: A total of 170 participants completed Round 1 of the Delphi survey, and 116 completed Round 2. Respondents came from 29 countries, with 49% being patients/caregivers. Results showed that 14 out of the 17 subdomains within the 7 domains exceeded the 70% criterion (endorsement ranged from 83% to 100% of respondents). At OMERACT 2016, only 8% of the 96 attendees were patients/caregivers. Despite initial votes of support in breakout groups, there was insufficient comfort about the conceptualization of these 7 domains and 17 subdomains for these to be endorsed at OMERACT 2016 (endorsement ranged from 17% to 68% of participants). CONCLUSION: Differences between the Delphi survey and consensus meeting may be explained by the manner in which the outcomes were presented, variations in participant characteristics, and the context of voting. Further efforts are needed to address the limited understanding of SDM and its outcomes among OMERACT participants.
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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 | 0.393 | 0.323 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".