Can Shared Decision Making Help Eliminate Disparities in Rheumatoid Arthritis Outcomes?
Bibliographic record
Abstract
In this issue of The Journal , Barton, et al , publish a study on the quality of shared decision making between providers and patients with rheumatoid arthritis (RA) followed in 2 northern California cohorts1. Why is such a study important in what seems like a golden age of RA treatment? After all, there has been an explosion in RA treatment options and strategies, which has made remission a realistic target2. Earlier, more aggressive, and better treatment of RA has resulted in greatly improved outcomes compared to past decades3. Barton, et al ’s study is not only important but timely, because not all have shared equally from the therapeutic benefits of the biologic era. There is abundant evidence of racial and ethnic disparities in RA outcomes in the United States. Bruce, et al demonstrated that white patients with RA had less disability and better global health compared to nonwhite patients with RA4. Barton, et al reported lower disease activity and better functional status in whites, anglophones, and non-foreign-born patients in a university rheumatology clinic5. Greenberg, et al recently published their findings from the Consortium of Rheumatology Researchers of North America RA … Address correspondence to Dr. J. Hirsh. E-mail: joel.hirshMD{at}dhha.org
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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 | 0.012 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.021 | 0.025 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".