The Hidradenitis Suppurativa Priority Setting Partnership
Bibliographic record
Abstract
BACKGROUND: Hidradenitis suppurativa (HS) has been neglected by medical researchers and society in general, despite being a relatively common, painful, chronic skin disease. OBJECTIVES: To generate a top 10 list of HS research priorities, from the perspectives of patients with HS, carers and clinicians, to take to funding bodies. METHODS: A priority setting partnership was established between patients with HS, carers and clinicians, following the James Lind Alliance process. Survey 1 requested submission of HS uncertainties, which were grouped into 'indicative uncertainties' for prioritization in survey 2. The 30 highest-ranked indicative uncertainties were reduced to a 'top 10' list using nominal group technique at a prioritization workshop attended by all relevant HS stakeholders. RESULTS: In total 1495 potential uncertainties were submitted in survey 1, including 57% from patients with HS and carers, and grouped into 55 indicative uncertainties. Ranking in survey 2 was completed by 371 participants, 50% of whom were patients and carers. The final workshop was attended by 22 HS stakeholders and four facilitators and produced a top 10 list, the three highest priorities in descending order being (i) What is the most effective and safe group of oral treatments in treating HS? (ii) What is the best management of an acute flare? (iii)What is the impact of HS and its treatment on people with HS? CONCLUSIONS: The top 10 HS research priorities have been directly disseminated to funders to raise awareness of HS. The next step is to generate research questions that will provide the evidence needed to improve care for patients with 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 | 0.175 | 0.124 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".