Factors Explaining the Discrepancy Between Physician and Patient Global Assessment of Joint and Skin Disease Activity in Psoriatic Arthritis Patients
Bibliographic record
Abstract
OBJECTIVE: To assess the extent and determinants of discordance in scoring between patient global assessment (PtGA) and physician global assessment (PhGA) in patients with psoriatic arthritis (PsA). METHODS: A cross-sectional and longitudinal analysis of data was conducted in patients attending a large PsA clinic. The difference between PtGA and PhGA (each measured on a scale of 0-10, with 0 indicating best status and 10 indicating worst status) reflected the discrepancy between the PtGA and PhGA of joint and skin activity and could take values from -10 (higher rating of disease activity by the patient) to 10 (higher rating of disease activity by the physician). Multivariate regression identified variables that contributed significantly to each of the outcomes. The proportion of variability of each outcome explained by each predictor was expressed by the partial R(2) . RESULTS: A total of 565 patients were included in the analysis. Patients tended to score their disease worse than their physicians, with greater discordance for the joints than for the skin (mean ± SD 1.68 ± 2.41 PtGA-PhGA difference for joints, and 0.77 ± 2.66 for skin). Fatigue accounted for 21% of the variation in the difference between PtGA and PhGA for joints. Pain (Rpartial2 = 9%) and disability by Short Form 36 health survey (Rpartial2 = 1.2%) were also important factors, each of which led to higher patient rating; whereas increased tender joint count (Rpartial2 = 16%) and swollen joint count (Rpartial2 = 1.4%) resulted in a higher physician rating of arthritis. CONCLUSION: Fatigue, pain, disability, and tender and swollen joint counts were the most important factors contributing to discrepancy between patient and physician assessment of joint activity.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".