Light and Laser-based Treatments for Hidradenitis Suppurativa: A Systematic Review
Bibliographic record
Abstract
Background: Hidradenitis suppurativa (HS) is characterized by painful, recurrent lesions occurring mainly in intertriginous areas. The pain, odor, and disfigurement caused by HS significantly impacts quality of life and is challenging to treat. A comprehensive systematic review evaluating the use of light and laser-based treatments for HS is lacking. Methods: We performed a systematic review by searching Cochrane, MEDLINE and Embase. Title, abstract and full text screening, and data abstraction were done in duplicate. Results: Forty studies met the inclusion criteria, representing a total of 821 patients. Included studies were comprised of 5 randomized within-patient controlled trials, 1 randomized controlled trial, and 34 case series. Overall, treatments with the most reported cases were laser surgery, photodynamic therapy (PDT), and laser field treatments which showed a response in 80% (n=344/431), 73% (n=122/167) and 71% (n=84/101) of treated patients respectively. The pooled response rate for psoralen plus ultraviolet A was 69% (n=9/13). Conclusion: Our results suggest that laser surgery using carbon dioxide (CO2) laser or a combination of CO2 and Nd:YAG lasers has a moderate response rate for HS with the most reported cases. Laser for field treatment and PDT also had moderate response rates with a large number of reported cases. However, extrapolation of these results may be limited due to the majority of the studies being case series, lack of standardized outcomes being assessed, and insufficient long term follow up results.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".