Experience with ustekinumab for the treatment of moderate to severe Hidradenitis suppurativa
Bibliographic record
Abstract
BACKGROUND: Hidradenitis suppurativa (HS) is a severe chronic inflammatory follicular disease characterized by nodules and abscesses affecting apocrine gland-bearing regions. HS is not well-controlled with conventional medical therapies such as topical therapy, oral antibiotics and retinoids, however, abrogation of tumour necrosis factor-α (TNF-α) function has proven effective in some patients. OBJECTIVE: To assess the safety and efficacy of the interleukin-12/23 inhibitor, ustekinumab for treatment of HS in three patients with moderate-severe disease. METHODS: The subjects received 3-45 mg subcutaneous injections of ustekinumab at 0, 1 and 4 months. Improvement was assessed by the dermatology life quality index (DLQI), visual analogue scale of pain (VAS) and physician's global assessment (PGA) at each monthly visit. RESULTS: Prior to treatment, subjects had moderate-severe HS (Hurley stage II-III) with a DLQI score between 8 and 12. At 6 months, one patient showed complete disease remission, while a 25-49% improvement was seen in a second patient and no change in a third. A moderate but statistically significant relationship was observed between VAS and DLQI scores (r=0.75; P<0.01). CONCLUSION: Ustekinumab may provide a safe and effective new treatment strategy for HS in some patients. Interleukin 12/23 inhibition is a potential therapeutic option for patients in which other therapies prove ineffective.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".