Population-based Clinical Practice Research Datalink study using algorithm modelling to identify the true burden of hidradenitis suppurativa
Bibliographic record
Abstract
Summary Hidradenitis suppurativa (HS) is a long-term skin disease affecting young adults, causing multiple boils in skin crease sites such as the armpits and groins. The boils are painful, may produce pus and leave disfiguring scars. How common HS is remains controversial, with recent reports using USA medical insurance data suggesting about 0.1% of the population is affected, which is lower than European studies using self-reported questionnaires (completed by the patient). This may be because insurance databases miss undiagnosed cases. Our study team based in the UK aimed to use UK electronic data recorded by General Practitioners (GPs) to identify known and previously undiagnosed cases of HS. We identified undiagnosed cases by looking for patients who had seen their GP for at least 5 skin boils and validated their diagnosis by sending some of the GPs a questionnaire to double-check. Out of 4.3 million patients in the GP database, we found 23,000 diagnosed HS patients and 10,000 undiagnosed patients, showing that 0.77% of the UK population has HS. Including probable cases, who had 1-4 skin boil consultations, the figure rises to 1.19%. Comparing people with HS to similar people without HS, there are higher rates of smoking and obesity (both 3 times more common), as well as type 2 diabetes, Crohn's disease, raised fat levels in the blood, acne, high blood pressure and depression. However a link was not found between HS and ulcerative colitis or polycystic ovary syndrome. In conclusion, we found that HS is relatively common, nearly 10 times more common than the estimates using USA insurance data. People with HS have higher rates of risk factors for heart disease and stroke and so checking for these conditions is important.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.028 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".