Combination Therapies for Hidradenitis Suppurativa: A Retrospective Chart Review of 31 Patients
Bibliographic record
Abstract
BACKGROUND: Although a variety of medical and surgical interventions exist for the treatment of hidradenitis suppurativa (HS), it remains a challenging disease to manage because of its variable presentation and unpredictable clinical course. Apart from the combination of clindamycin and rifampin, the success of other combination therapies is largely unknown. OBJECTIVES: The goal of our study was to examine the clinical utility of various combination therapies for the treatment of HS. METHODS: We conducted a qualitative retrospective chart review of 31 patients with dermatologist-diagnosed HS who were seen at an academic teaching hospital between 2014 and 2018. Demographic data, disease location, disease severity, and treatment protocol were retrieved for analysis. Hurley stage was used to classify disease severity on initial presentation, and the International Hidradenitis Suppurativa Severity Score System (IHS4) was used to track changes across visits. RESULTS: = 37.7 years; 67.7% female) included in the study, 6 (19.4%), 11 (35.5%), and 14 (45.2%) patients were classified as Hurley stages I, II, and III, respectively. Although no statistical results are provided because of the small sample size, we have identified several drug combinations that show promising clinical response for patients with HS based on their IHS4 score, such as isotretinoin/spironolactone for mild disease, isotretinoin or doxycycline with adalimumab for moderate disease, and cyclosporine/adalimumab for severe disease. CONCLUSIONS: This preliminary work demonstrates that HS treatment with combination therapy appears to be a promising method of disease management.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".