145 The British Society for Rheumatology’s Choosing Wisely UK recommendations
Bibliographic record
Abstract
Background: The Choosing Wisely UK campaign aims to promote shared decision making between patients and clinicians, helping people choose care that is supported by evidence, free from harm, truly necessary and consistent with their values. The Academy of Medical Royal Colleges (AoMRC), which coordinates the campaign, invited the BSR to submit 3-6 recommendations in 2018. The audience includes patients; rheumatologists and other physicians; GPs; nurses ad allied health professionals. Methods: The 14-member working group included two patient contributors, one consultant nurse, six consultant rheumatologists, one GP staff grade rheumatologist, two rheumatology trainees and two immunologists. The National Rheumatoid Arthritis Society and Versus Arthritis were represented. The working group was convened and recommendation development completed within 12 weeks. For the first part of the abbreviated Delphi-exercise, working group members submitted proposed recommendations with an accompanying evidence summary. These were collated and distributed (verbatim and anonymously) to the group to inform a ranking exercise. Members rated each topic from 5 (highest) to 1 (lowest) anonymously and left remaining topics unscored; topics with the highest scores were selected. A subgroup, including patient contributors, met to draft the recommendations. Evidence summaries were collated from information submitted in the initial proposals and from further contributions from working group members. External experts were consulted on each recommendation, following which consensus was sought from the working group to ratify the recommendations. Results: Thirty-two proposals were received on 14 discrete clinical topics, from 10 working group members. Twelve members ranked topics. Six final recommendations were developed, all of which were endorsed by the BSR. The AoMRC accepted all six recommendations, proposing that ANA+ENA and C3/C4/dsDNA had clinician facing-recommendations only, due to their technical nature (table 1). Conclusion: Six recommendations were developed by a multidisciplinary team including people with arthritis. Because of the robust development process, we believe these recommendations are acceptable, meaningful and practical. Their application will lead to more personalised care, increased patient and clinician satisfaction, and better use of limited resources. We encourage all BSR members to engage with and champion these recommendations to inform shared decision-making conversations with patients. BSR Choosing Wisely UK Recommendations Disclosures: C.A. Sharp: Grants/research support; Charlotte A Sharp is supported by the National Institute for Health Research Collaboration for Leadership in Applied Health Research and Care (NIHR CLAHRC) Greater Manchester. I.N. Bruce: Honoraria; Ian N Bruce has received honoraria and/or grant funding from GSK, Eli Lilly, Astra Zeneca and Merck Serono. Grants/research support; Ian N Bruce has received honoraria and/or grant funding from GSK, Eli Lilly, Astra Zeneca and Merck Serono. B.M. Ellis: None. S. Elkhalifa: None. J. Galloway: None. B. Mulhearn: None. J. Firth: None. J. Fox: None. C. Mukhtyar: None. D.J. Murphy: None. A. Rowbottom: None. N. Snowden: None. K. Staniland: None. E. MacPhie: Honoraria; E.M. has been sponsored to attend international meetings by Pfizer and Roche, has accepted honoraria for educational meetings from Pfizer and Roche, her department has received sponsorship from Pfizer.
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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 | 0.023 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.017 | 0.014 |
| Insufficient payload (model declined to judge) | 0.063 | 0.041 |
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".