Test-Retest Reliability of Patient Global Assessment and Physician Global Assessment in Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: As a guide to treatment of rheumatoid arthritis (RA), physicians use measurement tools to quantify disease activity. The Patient Global Assessment (PGA) asks a patient to rate on a scale how they feel overall. The Physician Global Assessment (MDGA) is a similar item completed by the assessing physician. Both these measures are frequently incorporated into other indices. We studied reliability characteristics for global assessments and compared test-retest reliability of both the PGA and the MDGA, as well as other commonly used measures in RA. METHODS: We studied 122 patients with RA age 17 years or older. Patients who received steroid injection or change in steroid dose at the visit were excluded. Patients completed the HAQ, PGA, visual analog scale for pain (VAS Pain), VAS Fatigue, and VAS Sleep. After seeing their physician, they received another questionnaire to complete within 2 days at the same time of day as clinic visit. Physicians completed the MDGA at the time of the patient's appointment and at the end of their clinic day. Test-retest results were assessed using intraclass correlations (ICC). "Substantial" reliability is between 0.61-0.80 and "almost perfect" > 0.80. RESULTS: Four rheumatologists and 146 patients participated, with 122 questionnaires returned (response rate 83.6%). Test-retest reliability was 0.702 for PGA, 0.961 for MDGA, and 0.897 for HAQ; VAS results were 0.742 for Pain, 0.741 for Fatigue, and 0.800 for Sleep. The correlation between PGA and MDGA was -0.172. CONCLUSION: PGA, MDGA, HAQ, and VAS Pain, VAS Fatigue, and VAS Sleep all showed good to excellent test-retest reliability in RA. MDGA was more reliable than PGA. The correlation between PGA and MDGA was poor.
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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 | 0.021 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
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".