Approach to the Management of Patients With Hidradenitis Suppurativa: A Consensus Document
Bibliographic record
Abstract
BACKGROUND: Hidradenitis suppurativa (HS) is a painful, debilitating, and poorly understood condition, which is suboptimally diagnosed, managed, and treated. Evidence supporting various treatment modalities is sparse. OBJECTIVES: To incorporate scientific evidence and expert opinions to develop useful guidance for the evaluation and management of patients with HS. METHODS: An expert panel of Canadian dermatologists and surgeons developed statements and recommendations based on available evidence and clinical experience. The statements and recommendations were subjected to analysis and refinement by the panel, and voting was conducted using a modified Delphi technique with a prespecified cutoff agreement of 75%. RESULTS: Ten specific statements and recommendations were accepted by the expert panel. These were grouped into 4 domains: diagnosis and assessment, treatment and management, comorbidities and a multidisciplinary approach, and education. CONCLUSIONS: These statements and recommendations will serve to increase awareness of HS and provide a framework for decisions involving diagnosis and management. Evidence suggests that antibacterial and anti-tumour necrosis factor therapies are effective in the treatment of HS. This is supported by the clinical experience of the authors. Further clinical research and the establishment of multidisciplinary management teams will continue to advance management of HS in Canada.
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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 | 0.016 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".