Impact of Medical and Surgical Treatments on Quality of Life and Pain for Hidradenitis Suppurativa: A Systematic Review and Meta Analysis
Bibliographic record
Abstract
To the Editor, Hidradenitis suppurativa (HS) is a chronic inflammatory skin disease that can significantly affect patients' quality of life (QoL), causing pain, discomfort, and emotional distress.1,2 With emerging therapies for HS, reporting patientreported outcomes (PROs) in studies is crucial, as QoL and pain remain primary considerations in treatment selection.3 This systematic review and meta-analysis evaluates the impact of current HS treatments on QoL and other PROs in observational studies.MEDLINE, Embase, CENTRAL, and LILACS were searched from inception to April 2024, using keywords related to HS and PROs.Primary literature on medical, surgical, and other treatments for HS were included (Figure S1).Study characteristics, baseline, and post-intervention PRO scores were extracted.Where data were sufficient, a randomeffects meta-analysis was conducted to determine the impact of HS treatments on QoL and pain.Results were reported as standardized mean differences (SMD) for generalizability and clinical relevance.The risk of bias was assessed with Joanna Briggs Institute checklists and the Cochrane risk of bias tool according to a pre-registered PROSPERO protocol (CRD42024542774).From 43 studies, 1763 patients (mean age: 36.7 years) with female predominance (58.1%) were identified.Most had Hurley stage II/III disease (82.6%); baseline Dermatology Life Quality Index (DLQI), pain Numerical Rating Scale, and Visual Analog Scale (VAS) scores averaged 15.3/30, 6.0/10, and 5.8/10, respectively, indicating a significant QoL impairment and moderate pain (Table S1).Intralesional kenalog, resorcinol, Janus kinase inhibitors, hyperbaric oxygen, photodynamic therapy, laser, and combination treatments were analyzed descriptively (Tables S2-S8).Meta-analysis revealed a large improvement in DLQI scores for all treatments (SMD >0.8).Among medical interventions, anti-TNF-α inhibitors (SMD 1.28-1.46,95% CIs
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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 | 0.023 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.019 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".