Standardizing health outcomes for people with rheumatoid arthritis receiving disease modifying drug therapy: A rapid review of patient-decision aids and preference studies to inform the development of OMERACT Health Outcome Descriptors
Bibliographic record
Abstract
BACKGROUND: Interest in standardizing descriptions of health outcomes is increasing. In a Health Outcome Descriptor (HOD), outcomes are systematically described covering four domains: Symptoms, Time horizon, Testing and Treatment, and Consequences. Given the lack of HODs for Rheumatoid Arthritis (RA), the aim of this study was to review published RA outcome descriptions and map them to the HOD framework. METHODS: We conducted a rapid review of patient-decision aids (PtDAs) and patient preference studies to assess how seven RA outcomes have been described in English to patients. These outcomes were selected by author consensus, from a living systematic review of RA drug therapy. After data extraction and a thematic content analysis, a narrative summary for each outcome was provided. RESULTS: We included 11 PtDAs and 27 patient preference studies. Overall, the descriptions of the same health outcome varied widely across studies. Adverse events (AEs) were described in most cases (N = 26/38). For both PtDAs and preference studies, few provided a description for patient-important outcomes like remission (N = 2/11 and N = 1/27 respectively) and pain (N = 2/11 and N = 6/27 respectively). From an HOD perspective, the descriptions focused primarily on symptoms patients may experience (94 %), and less on the other domains (18-38 %). CONCLUSION: There is wide variability in the content of the published RA outcome descriptions, as well as a lack of descriptions regarding common patient-important outcomes. As this study provides a detailed overview of existing descriptions, it may inform future development of HODs for RA.
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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 | 0.141 | 0.331 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.032 | 0.023 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".