A Real-World Analysis of Weather Variation on Disease Activity and Patient-Reported Outcomes in Psoriatic Arthritis
Bibliographic record
Abstract
OBJECTIVE: Patients with inflammatory articular diseases, such as psoriatic arthritis (PsA), report weather changes in their symptoms. Our objective was to investigate the correlation between weather variation, disease activity (DA), and patient-reported outcomes (PROs) in patients with PsA. METHODS: Hourly measurements of temperature, relative humidity, and pressure were obtained from 2015 to 2020 in Montreal (through Environment Canada) and were matched with DA and PROs of patients with PsA enrolled in Rhumadata. The differences in mean DA and PROs were examined between winter and summer. Pearson correlation coefficients were calculated between clinical profile and weather measurements. RESULTS: < 0.001) were lower in winter. In summer, positive correlations were found between humidity and symptoms (using patient global assessment, fatigue, pain, C-reactive protein, Bath Ankylosing Spondylitis Disease Activity Index, Bath Ankylosing Spondylitis Functional Index), whereas negative correlations between temperature and Health Assessment Questionnaire-Disability Index were reported. In winter, positive correlations were observed between temperature, fatigue, and pain. CONCLUSION: This is the first study to investigate weather variations through subjective and objective PROs matched with patients with PsA. Statistically significant differences in clinical profile were evident between winter and summer, as well as in their correlation with weather measurements. However, these distinctions lack clinical significance, suggesting a small impact on patients with PsA.
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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 | 0.004 |
| 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 | 0.001 | 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".