Factors Influencing Raynaud Condition Score Diary Outcomes in Systemic Sclerosis
Bibliographic record
Abstract
OBJECTIVE: Raynaud phenomenon (RP) in systemic sclerosis (SSc) could be influenced by clinical phenotype, environmental factors (e.g., season), and personal factors (e.g., coping strategies and ill-health perceptions). We studied the relative influence of a range of putative factors affecting patient-reported assessment of SSc-RP severity. METHODS: SSc patients were enrolled at UK and US sites. Participants completed the 2-week Raynaud Condition Score (RCS) diary alongside collection of patient demographics, clinical phenotype, the Coping Strategies Questionnaire, Pain Catastrophizing Scale, Scleroderma Health Assessment Questionnaire (SHAQ), and both patient/physician visual analog scale (VAS) assessments for RP, digital ulcer disease, and global disease. Environmental temperature data were obtained at each site. A second RCS diary was completed 6 months after enrollment. RESULTS: We enrolled 107 patients (baseline questionnaires returned by 94). There were significant associations between RCS diary variables and both catastrophizing and coping strategies. There were significant associations between RCS diary outcomes and both environmental temperature and season of enrollment. Age, disease duration, sex, disease subtype, smoking, and vasodilator use were not associated with RCS diary outcomes. The best-fitting multivariate model identified the patient RP VAS, SHAQ pain VAS, and SHAQ gastrointestinal VAS subscales as the strongest independent predictors of the RCS. CONCLUSION: Patient-reported assessment of SSc-RP severity is associated with a number of factors including pain, catastrophizing, and coping strategies. The effects of seasonal variation in environmental temperature on SSc-RP burden has implications for clinical trial design. Treatments targeting SSc-RP pain and the development of behavioral interventions enhancing coping strategies may reduce the burden of SSc-RP.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".