Population-based Clinical Practice Research Datalink study using algorithm modelling to identify the true burden of hidradenitis suppurativa
Bibliographic record
Abstract
Summary Background Epidemiology data regarding hidradenitis suppurativa (HS) are conflicting and prevalence estimates vary 80-fold, from 0·05% in a population-based study to 4%. Objectives To assess the hypothesis that previous population-based studies underestimated true HS prevalence by missing undiagnosed cases. Methods We performed a population-based observational and case–control study using the U.K. Clinical Practice Research Datalink (CPRD) linked to hospital episode statistics data. Physician-diagnosed cases in the CPRD were identified from specific Read codes. Algorithms identified unrecognized ‘proxy’ cases, with at least five Read code records for boils in flexural skin sites. Validation of proxy cases was undertaken with general practitioner (GP) questionnaires to confirm criteria-diagnosed cases. A case–control study assessed disease associations. Results On 30 June 2013, 23 353 physician-diagnosed HS cases were documented in 4 364 308 research-standard records. In total, 68 890 proxy cases were identified, reduced to 10 146 criteria-diagnosed cases after validation, extrapolated from 107 completed questionnaires (61% return rate). Overall point prevalence was 0·77% [95% confidence interval (CI) 0·76–0·78%]. An additional 18 417 cases had a history of one to four flexural skin boils. In physician-diagnosed cases, odds ratios (ORs) for current smoker and obesity (body mass index > 30 kg m-2) were 3·61 (95% CI 3·44–3·79) and 3·29 (95% CI 3·14–3·45). HS was associated with type 2 diabetes, Crohn disease, hyperlipidaemia, acne and depression, and not associated with ulcerative colitis or polycystic ovary syndrome. Conclusions Contrary to results of previous population-based studies, HS is relatively common, with a U.K. prevalence of 0·77%, one-third being unrecognized, criteria-diagnosed cases using the most stringent disease definition. If individuals with probable cases are included, HS prevalence rises to 1·19%.
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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 | 0.049 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".