Gender Differences in Perceptions of Psoriatic Arthritis Disease Impact, Management, and Physician Interactions: Results from a Global Patient Survey
Bibliographic record
Abstract
INTRODUCTION: We evaluated the impact of gender on disease severity, health-related quality of life (HRQoL), treatment management, and patient-healthcare professional (HCP) interactions from the perspectives of patients with psoriatic arthritis (PsA). METHODS: Data were collected from a global online patient survey conducted by The Harris Poll (November 2, 2017 to March 12, 2018). Eligible patients were aged ≥ 18 years, with a self-reported diagnosis of PsA for > 1 year, had visited a rheumatologist/dermatologist in the past 12 months, and had reported previously using ≥ 1 conventional synthetic or biologic disease-modifying antirheumatic drug. Data were stratified by gender and analyzed descriptively, inferentially by binomial (chi-square) tests, and by multivariate logistic regression models. RESULTS: Data from 1286 patients who participated were included: 52% were female, 48% were male. Varying perceptions of disease severity between males and females were indicated by differences in symptoms leading to a diagnosis of PsA, and in symptoms reported despite treatment; more females than males reported joint tenderness, skin patches/plaques, and enthesitis. More females than males reported a major/moderate impact of PsA on their physical activity and emotional/mental well-being. Reasons for switching medication differed between genders, with more females switching because they perceived their medication to not be effective enough related to their joint symptoms. More females than males were very satisfied with their communication with their rheumatologist and were more likely to discuss the impact of PsA on their daily lives, their treatment satisfaction, and treatment goals with their rheumatologist. CONCLUSIONS: Patients' perceptions of the impact of PsA on HRQoL, treatment management, and interactions with HCPs varied between males and females. More females than males reported major/moderate physical and emotional impacts of PsA. When treating patients, it is important for HCPs to consider the potential impact of gender on patients' experience of PsA and its symptoms. Graphical plain language summary available for this article.
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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 | 0.002 | 0.004 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".