Clinical Practice Guidelines: Incorporating Input From a Patient Panel
Bibliographic record
Abstract
OBJECTIVE: To describe the integral role of a Patient Panel in the development of the 2017 American College of Rheumatology (ACR)/American Association of Hip and Knee Surgeons (AAHKS) clinical practice guideline. METHODS: We convened a Panel of 11 patients with rheumatoid arthritis and juvenile idiopathic arthritis, all of whom had undergone 1 or more arthroplasties, to review the evidence and provide guidance on recommendations for the 2017 ACR/AAHKS guideline to address the perioperative management of antirheumatic medication in patients with rheumatic diseases undergoing elective total hip or total knee arthroplasty. The guideline used the Grading of Recommendations Assessment, Development, and Evaluation methodology that acknowledges the critical role of patient values and preferences when the quality of the evidence base is low or when there are important trade-offs between benefits and harms. The Patient Panel considered the relative importance of complications including perioperative infection versus rheumatic disease flare and voted on the recommendations. Before the Voting Panel's own discussion of the recommendations, they reviewed a summary of the Patient Panel's discussion, including their perioperative experience, the relative importance they placed on infections versus flares in the perioperative period, and their votes on the recommendations. RESULTS: The Patient Panel placed higher importance on avoiding an infection than a disease flare despite the far greater frequency of flares than infections. The decisions of the Voting Panel were concordant with those of the Patient Panel. For the 7 recommendations that both Panels voted on, the Panels agreed on the direction as well as the strength of recommendation (which was conditional for all recommendations). CONCLUSION: The Voting Panel considered the importance that the patients placed on risk of infection. The Patient Panel's values informed the direction and strength of the recommendations in the final 2017 ACR/AAHKS guideline.
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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 | 0.277 | 0.497 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.020 | 0.022 |
| Insufficient payload (model declined to judge) | 0.013 | 0.009 |
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".