Discordance of Global Assessments by Patient and Physician Is Higher in Female than in Male Patients Regardless of the Physician’s Sex: Data on Patients with Rheumatoid Arthritis, Axial Spondyloarthritis, and Psoriatic Arthritis from the DANBIO Registry
Bibliographic record
Abstract
OBJECTIVE: To assess the frequency of discordance in patient's (PtGA) and physician's (PGA) global assessment, and to investigate whether higher discordance in female patients compared with male patients is associated with the physician's sex in patients with rheumatoid arthritis (RA), axial spondyloarthritis (axSpA), and psoriatic arthritis (PsA). METHODS: PtGA, PGA, and other patient-related variables were retrieved from the Danish DANBIO registry, used nationwide to monitor patients with RA, axSpA, and PsA. A questionnaire was sent to all physicians registering in DANBIO (n = 265) regarding individual physician characteristics including sex and age. Discordance was defined as PtGA > 20 mm higher (or lower) than PGA. First encounters between patients and physicians were analyzed using descriptive statistics and mixed model regression analysis. RESULTS: Ninety physicians (34%) returned the questionnaire and were pairwise matched with 10,282 first patient encounters (8300 patients with RA, 524 axSpA, and 1458 PsA). The frequency of discordant (PtGA > PGA) encounters (not including PGA > PtGA seen in < 2%) in RA, axSpA, and PsA was 49.0%, 48.3%, and 56.5%, respectively. Discordance was more common in female patients with high scores on functional disability, pain, and fatigue across the 3 diseases, whereas it was independent of the physician's sex. CONCLUSION: In this study on Danish patients with RA, axSpA, and PsA, the PtGA was > 20 mm higher than the PGA in about half of the encounters, and more common in female patients of both female and male physicians. This finding highlights one of the challenges in shared decision making.
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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 | 0.001 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".