Prevalence, severity, and clinical correlates of pain in patients with systemic sclerosis
Bibliographic record
Abstract
OBJECTIVE: Large descriptive studies of pain in systemic sclerosis (SSc) are lacking. The present study estimated prevalence, severity, and associations between SSc clinical variables and pain in all patients with SSc and in limited cutaneous (lcSSc) and diffuse cutaneous (dcSSc) subsets. METHODS: Patients enrolled in a multicenter SSc registry (n = 585) completed a standardized clinical assessment and questionnaires about their physical and psychosocial health, including a pain severity numerical rating scale (NRS; range 0-10). Pain prevalence and severity were estimated with descriptive statistics. Crude and adjusted associations between specific SSc clinical variables and pain were estimated with linear regression for the entire group and by SSc subtype. RESULTS: Of the patients, 484 (83%) reported pain (268 [46%] mild pain [NRS 1-4], 155 [27%] moderate pain [NRS 5-7], and 61 [10%] severe pain [NRS 8-10]). More frequent episodes of Raynaud's phenomenon, active ulcers, worse synovitis, and gastrointestinal (GI) symptoms were associated with pain in multivariate analysis adjusting for demographic variables, depressive symptoms, and comorbid conditions. Patients with dcSSc reported only slightly higher mean +/- SD pain than those with lcSSc (dcSSc 3.9 +/- 2.8 versus lcSSc 3.4 +/- 2.7; Hedges's g = 0.18, P = 0.05). Regression estimates did not differ significantly between SSc subsets. CONCLUSION: Pain symptoms were common in the present study of patients with SSc and were independently associated with more frequent episodes of Raynaud's phenomenon, active ulcers, worse synovitis, and GI symptoms. Subsetting by extent of skin involvement was only minimally related to pain severity and did not affect associations with clinical variables. More attention to pain and how to best manage it is needed in SSc.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".