Prevalence of Musculoskeletal Symptoms in Patients With Hidradenitis Suppurativa and Associated Factors: Cross-Sectional Study
Bibliographic record
Abstract
The prevalence of and factors associated with musculoskeletal (MSK) symptoms in patients with hidradenitis suppurativa (HS) have yet to be elucidated. Given the association between HS and inflammatory comorbidities, understanding the burden of MSK symptoms in patients with HS is crucial for patient-centered care. Our objective was to describe the prevalence of and factors associated with MSK symptoms in patients with HS. A cross-sectional study of 78 consecutive patients recruited between November 2021 and February 2023 with a dermatology-confirmed diagnosis of HS, irrespective of MSK symptoms, was performed. The average age of participants (n=78) was 37 (SD 12.2) years, and the average age at symptom onset was 23 (SD 12.1) years; 54% (n=42) of participants identified as women, and 46% (n=36) as men. The most common comorbidities included depression (n=17, 22%) and preexisting arthritis (n=12, 16%). Approximately 24% (n=18) of participants reported prolonged morning stiffness. In a multivariate regression, depression was significantly associated with morning stiffness (odds ratio [OR] 6.1, 95% CI 1.4-26.1; P=.02), while female sex was significantly associated with arthralgia (OR 19.1, 95% CI 1.6-235.2; P=.02). Every patient with depression reported arthralgia. We highlight the high prevalence of MSK symptoms among patients with HS and note the interplay between depression and MSK symptoms, with each one potentially contributing to the other.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".