Evolving and evaluating the OMERACT fellows program: insights and implications from OMERACT 2023 fellows
Bibliographic record
Abstract
OBJECTIVE: To describe the evolution of the OMERACT Fellows Program (OM FP) and to evaluate the innovative changes implemented in the 2023 program. METHODS: The OM FP, the first of its kind in global rheumatology, was developed in 2000 to mentor early career researchers in methods and processes for reaching evidence-driven consensus for outcome measures in clinical studies. The OM FP has evolved through continuing iterations of face to face and online feedback. Key new features delivered in 2023 included e-learning modules, virtual introductory pre-meetings, increased networking with Patient Research Partners (PRPs), learning opportunities to give and receive personal feedback, ongoing performance feedback during the meeting from Fellow peers, PRPs, senior OMERACTers (members of the OMERACT community) and Emerging Leader mentors, involvement in pitching promotions, two-minute Lightning Talks in a plenary session and an embedded poster tour. An online survey was distributed after the meeting to evaluate the program. RESULTS: OM FP has included 208 fellows from 16 countries across 4 continents covering 47 different aspects of rheumatology outcomes since its inception. Over 50 % have remained engaged with OMERACT work. In 2023, 18 Fellows attended and 15 (83 %) completed the post-meeting survey. A dedicated OM FP was deemed important by all respondents, and 93 % would attend the meeting in future. The PRP/Fellow Connection Carousel and Lightning Talks were rated exceptional by 93 %. Key components to improve included clarification of expectations, overall workload, the Emerging Leaders Mentoring Program, and the content and duration of daily summary sessions. CONCLUSION: The innovations in the 2023 OM FP were well received by the majority of participants and supports early career rheumatology researchers to develop collaborations, skills and expertise in outcome measurement. Implementation of feedback from Fellows will enhance the program for future meetings, continuing to facilitate learning and succession planning within OMERACT.
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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 | 0.134 | 0.123 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".