Hidradenitis suppurativa diagnosis and management in primary care: not just recurrent boils
Bibliographic record
Abstract
Hidradenitis suppurativa (HS) is characterised by inflamed skin lesions and scars that occur in apocrine-gland-bearing areas including axillae, inframammary folds, and groin, with a chronic course including flares.1 It is associated with an average diagnostic delay of 7.2 years and has a Western population prevalence of approximately 1%–2%.2,3 HS affects young to middle-aged adults and is more common in women. It is 2–3 times more common in people with skin of colour especially those of African American descent.2 HS significantly impacts general health and quality of life. A recent cross-sectional survey of GPs found that, while patients were largely managed appropriately, there was suboptimal management of pain, psychosocial aspects, and comorbidity screening.4 The current article aims to address this educational need. Historically, HS was poorly recognised and under-researched. Patients are often treated sub-optimally and comorbidities are not managed. Practitioners in primary care can both diagnose and treat early disease. They can address comorbidities, support lifestyle changes, and offer psychological support. Three main elements need to be considered in a diagnosis of 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".