International consensus statement on the use of ultrasound in hidradenitis suppurativa
Bibliographic record
Abstract
BACKGROUND: So far, the evidence on the use of ultrasound (US) in hidradenitis suppurativa (HS) demonstrates the utility of US in the diagnosis, scoring and assessment of HS; however, to date, there is no international consensus statement on the use of US in HS, and several published guidelines do not include this topic. OBJECTIVES: To create an international consensus statement on using US in HS that can cover and validate relevant aspects. METHODS: A three-round Delphi study with a panel of international experts representing four continents and working with US in HS in their daily practice. The inclusion criteria of the experts and the set of questions in the survey were defined by a steering committee. A consensus of recommendation was defined when the percentage of agreement (sum of strongly agree and agree) was ε 70%. In between 50% and 69% of agreement, a suggestion was considered. Lower than 50% meant no consensus. RESULTS: Twenty-four international experts from 14 countries participated in the study. A high percentage of consensus (96.4%) was achieved for important aspects of the use of US in HS, including the ultrasonographic indications, technical considerations, training, diagnostic criteria, staging systems, monitoring, support of US-guided procedures and planning of surgery, and the need for US in research and clinical trials. CONCLUSIONS: An international group of experts created a consensus statement with validated recommendations on the use of US in HS. Despite the challenges of the implementation of ultrasound in HS, this task force highly recommends the use of US in HS.
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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 | 0.228 | 0.257 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.009 | 0.011 |
| Research integrity | 0.015 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".