The responsiveness of generic health status measures as assessed in patients with rheumatoid arthritis receiving infliximab.
Bibliographic record
Abstract
OBJECTIVE: We used a variety of health status measures in 2 groups of patients with rheumatoid arthritis (RA) to assess both the smallest distinguishable difference and the relative responsiveness to change of these measures, when used in clinical practice. METHODS: Two groups of patients were studied. Group 1: 24 patients with stable RA tested on 2 occasions; Group 2: 60 patients receiving methotrexate tested before and 14 weeks after treatment with infliximab. Assessments were made with self-completed questionnaires: the modified Health Assessment Questionnaire, Medical Outcomes Study Short Form-36 [SF-36 (SF-6D)], EuroQol, and, in some, the standard gamble. Group 2 also had joint counts, and measures of erythrocyte sedimentation rate, C-reactive protein, and hemoglobin. RESULTS: The limits-of-agreement (Bland-Altman) approach had greater confidence intervals (CI) than did CI based on +/- 2 standard errors of the measurement. Improvement with infliximab could be determined with all measures, however, but the standard gamble seemed least responsive to change. CONCLUSION: The various measures had different degrees of responsiveness, but with all it was possible to show improvement in Group 2 compared to Group 1. There was a closer association of the patient centered measures of improvement with changes in pain score than with joint counts.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".