A Bayesian model that jointly considers comparative effectiveness research and patients’ preferences may help inform GRADE recommendations: an application to rheumatoid arthritis treatment recommendations
Bibliographic record
Abstract
OBJECTIVES: The objective of the study was to estimate the preferred treatment for early rheumatoid arthritis using a novel Bayesian approach that jointly considers patients' preferences and comparative effectiveness research. STUDY DESIGN AND SETTING: We estimated the preferred treatment using patients' preferences measured in a discrete-choice experiment to apply weights to benefit and harm outcomes from a network meta-analysis and other considerations (dosing, rare adverse events). Using Bayesian analyses, we considered the variability in patients' preferences and the imprecision in both patients' preferences and the treatment effects; all key considerations in the Grading of Recommendations Assessment, Development, and Evaluation approach. RESULTS: We estimated that most patients in our population would prefer triple therapy as initial treatment (78%) or after an inadequate response to methotrexate (62%). The probability of choosing triple therapy as initial treatment was further from 50% (the point of indifference) for more patients, making our prediction more confident, and suggesting a stronger recommendation could be made. After an inadequate response to methotrexate, the choice was more split, suggesting a decision aid may be helpful. CONCLUSION: Using a novel approach, we estimated that many patients with early rheumatoid arthritis may prefer triple therapy to other treatment options, in contrast to existing guidelines. This offers an approach that may help inform Grading of Recommendations Assessment, Development, and Evaluation treatment recommendations.
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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 | 0.143 | 0.368 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".