Physician’s Global Assessment in Psoriatic Arthritis: A Multicenter GRAPPA Study
Bibliographic record
Abstract
OBJECTIVE: Physician's global assessment (PGA) of disease activity is a major determinant of therapeutic decision making. This study assesses the reliability of the PGA, measured by means of 0-100 mm visual analog scale (VAS), and the additional use of separate VAS scales for musculoskeletal (PhysMSK) and dermatologic (PhysSk) manifestations in patients with psoriatic arthritis (PsA). METHODS: Sixteen centers from 8 countries enrolled 319 consecutive patients with PsA. PGA, PhysMSK, and PhysSk evaluation forms were administered at enrollment (W0) and after 1 week (W1). Detailed clinical data regarding musculoskeletal (MSK) manifestations, as well as dermatological assessment, were recorded. RESULTS: Comparison of W0 and W1 scores showed no significant variation (intraclass correlation coefficients were PGA 0.87, PhysMSK 0.86, PhysSk 0.78), demonstrating the reliability of the instrument. PGA scores were dependent on PhysMSK and PhysSk (p < 0.0001) with a major effect of the MSK component (B = 0.69) compared to skin (B = 0.32). PhysMSK was correlated with the number of swollen joints, tender joints, and presence of dactylitis (p < 0.0001). PhysSk scores were correlated with the extent of skin psoriasis and by face, buttocks or intergluteal, and feet involvement (p < 0.0001). Finally, physician and patient assessments were compared showing frequent mismatch and a scattered dot plot: PGA versus patient's global assessment (r = 0.36), PhysMSK versus patient MSK (r = 0.39), and PhysSk versus patient skin (r = 0.49). CONCLUSION: PGA assessed by means of VAS is a reliable tool to assess MSK and dermatological disease activity. PGA may diverge from patient self-evaluation. Because MSK and skin/nail disease activity may diverge, it is suggested that both PhysMSK and PhysSk are assessed.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".