Does Including Pain, Fatigue, and Physical Function When Assessing Patients with Early Rheumatoid Arthritis Provide a Comprehensive Picture of Disease Burden?
Bibliographic record
Abstract
OBJECTIVE: To explore the possibility of integrating patient-important outcomes like pain, fatigue, and physical function into the evaluation of disease status in early rheumatoid arthritis (ERA) without compromising correct disease activity measurement. METHODS: Patients from the 2-year Care in Early Rheumatoid Arthritis (CareRA) trial were included. Pain and fatigue (visual analog scales), Health Assessment Questionnaire (HAQ), standard components of disease activity [swollen/tender joint counts (SJC/TJC), C-reactive protein (CRP) or erythrocyte sedimentation rate (ESR), physician (PhGH) and patient (PaGH) global health] were recorded at every visit (n = 10). Pearson correlation and exploratory factor analyses (EFA), using multiple imputation (15×) and outputation (1000×), were performed per timepoint and overall, on standard components of disease activity scores with and without pain, fatigue, and HAQ. Each of the 15,000 datasets was analyzed using EFA with principal component extraction and oblimin rotation to determine which variables belong together. RESULTS: We included 379 patients. EFA on standard composite score components extracted 2 factors with no substantial cross-loadings. Still, pain (0.83), fatigue (0.65), and HAQ (0.59) were strongly correlated with PaGH. When rerunning the EFA with the inclusion of pain, fatigue, and HAQ, the 2-factor model had substantial cross-loadings between factors. However, a 3-factor model was optimal, with Factor 1: patient assessment, Factor 2: clinical assessment (PhGH, SJC, and TJC), and Factor 3: laboratory assessment (ESR/CRP). CONCLUSION: PaGH, pain, fatigue, and physical function represent a separate aspect of the disease burden of patients with ERA, which could be further explored as a target for care apart from disease activity. [ClinicalTrials.gov: NCT01172639].
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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 | 0.055 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".