The Prevalence and Severity of Pain in Patients With Systemic Sclerosis
Bibliographic record
Abstract
We investigated pain prevalence and severity in systemic sclerosis (SSc) and patient-physician perceptions. This was an internet-based survey that compared perceptions of pain type, location, severity, associated factors between patients with SSc and physicians, and pain treatment prescription patterns in Japan in March 2023. Data from 301 patients and 129 physicians revealed that 96.0% of patients experienced pain compared with 43.4% estimated by physicians. The median (interquartile range) Short-form McGill Pain Questionnaire (SF-MPQ-2) pain score was 47.0 (14.0-88.0). Continuous pain had the highest score (16.0 [3.0-27.0]), followed by neuropathic pain (14.0 [5.0-25.0]), intermittent pain (11.0 [1.0-25.0]), and affective descriptors (5.0 [1.0-14.0]). Pain at joints, fingertips, Raynaud's phenomenon (RP), and skin tightening were most prevalent across multiple pain types. Pain at fingertips and RP-affected locations were more common in limited cutaneous SSc (lcSSc) than in diffuse cutaneous SSc (dcSSc), and skin tightening was more common in dcSSc. Patients with dcSSc had significantly more severe pain than patients with lcSSc. Patients with nausea, insomnia, or diarrhea showed higher SF-MPQ-2 scores. Of the 129 physicians surveyed, 58.9% prescribed painkillers, 48.8% suggested self-care, 42.6% treated skin symptoms, and 16.3% referred patients to pain clinics for further management. Compared to the percentage of patients having pain in each location, physicians tend to be less aware of pain in the muscles, head, and abdomen. Most patients with SSc experience pain, which physicians tended to underestimate. Physicians' awareness of patients' experiences should be improved to provide adequate treatment for pain in SSc. Trial Registration: UMIN000050368.
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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 | 0.000 | 0.002 |
| 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".