What Does the Patient Global Health Assessment in Rheumatoid Arthritis Really Tell Us? Contribution of Specific Dimensions of Health‐Related Quality of Life
Bibliographic record
Abstract
OBJECTIVE: To estimate the contributions of health-related quality of life domains to the patient global assessment of disease activity (PtGA) in rheumatoid arthritis (RA). METHODS: Data are drawn from baseline visits of 2 observational RA cohorts. Participants completed forms for patient-reported outcome measures, including PtGA and measures from the Patient-Reported Outcomes Measurement Information System, and clinical data were collected. Factor analysis was used to identify latent variables, and multivariable linear regression was used to estimate determinants of the PtGA. RESULTS: Patients were mostly female (81%), white (78%), and had established disease (mean ± SD 12.3 ± 10.7 years), with 62% in remission or having low disease activity. In cohort 1 (n = 196), the following 2 factors emerged: 1) daily function (moderate-to-strong [i.e., >|0.65|] loadings of physical function, pain interference, social participation, and fatigue, and weak [>0.35] loadings of sleep disturbance); and 2) emotional distress (strong loadings of depression and anxiety). In crude analysis, daily function explained up to 53% and emotional distress up to 20% of the variance in PtGA. In both cohorts, in adjusted analyses, daily function and, to a much lesser extent, swollen joint count independently predicted PtGA; age was inversely related to PtGA in cohort 1 only. CONCLUSION: These findings suggest that in patients with RA, PtGA ratings largely reflect the extent to which patients feel they can function in everyday roles and are not impacted by mood. This suggests that higher than expected PtGA scores may offer an opportunity to discuss patient expectations regarding roles and activities and the impact of their RA symptoms on daily function.
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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 | 0.016 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".