Management of hidradenitis suppurativa in UK primary care: a cross-sectional survey
Bibliographic record
Abstract
BACKGROUND: Hidradenitis suppurativa (HS) is a painful, chronic, inflammatory skin condition affecting the skin folds. It is frequently misdiagnosed, leading to delays in care and the progression of the disease to permanent scarring. AIM: To understand the level of knowledge and confidence of healthcare professionals (HCPs) in primary care managing patients with HS. To establish their ability to recognise the early signs of HS, awareness of associated comorbidities, and recognition of treatment options available in primary care. DESIGN & SETTING: A survey was distributed to HCPs working in primary care in the UK. METHOD: The survey was disseminated via weekly GP bulletins distributed by local integrated care boards, the Primary Care Dermatology Society (PCDS) mailing lists, and at professional events. RESULTS: Of 183 responders, most (93%) did not have a specialist role in dermatology or a postgraduate qualification in dermatology (69%), 36 (20%) were not doctors, and there was a good geographical spread over the UK. Of the responders, 74% felt confident diagnosing HS, but only 39% were confident in managing the pain associated with the disease. Perceived confidence did not correlate with understanding the importance of early referral to secondary care where multiple skin sites were affected. CONCLUSION: Further education in diagnosing and managing HS in primary care is needed. Future research could focus on developing a tool to support the diagnosis of HS in primary care and a clear, primary care-focused management guideline for identified patients.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".