Cost-utility analysis of clinic-based deroofing versus local excision for hidradenitis suppurativa
Bibliographic record
Abstract
BACKGROUND: Deroofing and local excision are common clinic-based surgical options for hidradenitis suppurativa. Evidence suggests deroofing may have lower rates of adverse events (AEs), defined as disease recurrence or postsurgical complications. OBJECTIVE: This cost-utility analysis evaluates the economic and health-related impacts of clinic-based deroofing vs excision for hidradenitis suppurativa, comparing direct medical costs and quality-adjusted life-years (QALYs). METHODS: A Markov model was developed based on a literature review of clinical outcomes, EQ-5D utilities, and resource utilization. Patients began in a preprocedural state and transitioned monthly among 3 health states: responders (no AEs), nonresponders (≥1 AE), and death. The model assessed cost-effectiveness over a 2-year horizon from the U.S. healthcare system perspective. RESULTS: Deroofing provided an additional 0.19 QALYs at a cost of USD$311.39 per patient relative to excision, yielding a favorable incremental cost-effectiveness ratio of USD$1677.10/QALY, below the USD$50,000/QALY threshold. LIMITATIONS: Methodological constraints from limited published data were addressed through multiple sensitivity analyses. Cost-effectiveness was sensitive to AE rates, secondary costs, and utility values. CONCLUSION: When clinically appropriate, deroofing is more cost-effective than excision for clinic-based procedural management of HS, offering improved quality of life at a modest incremental cost.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".