Use of Clinical Public Databases in Hidradenitis Suppurativa Research
Bibliographic record
Abstract
In this viewpoint, we argue that recent studies using clinical public databases have revolutionized our understanding of hidradenitis suppurativa (HS), a chronic inflammatory skin condition with significant impacts on patients' quality of life. Our key messages are as follows: (1) these databases enable large-scale studies integrating genetic, epidemiological, and clinical data, providing crucial insights into HS's genetic predispositions, comorbidities, and treatment outcomes; (2) findings highlight a strong genetic component, with mutations in the γ-secretase complex playing a key role in HS pathogenesis and shaping targeted therapies; (3) studies also reveal elevated risks for comorbidities like obesity, diabetes, cardiovascular disease, and systemic inflammation in patients with HS, with diet-driven inflammatory pathways potentially exacerbating disease severity; (4) while these databases offer unprecedented research opportunities, limitations such as data representativeness and quality must be considered; (5) nonetheless, their benefits outweigh potential drawbacks, allowing the identification of rare comorbidities, disease progression patterns, and personalized treatment strategies; and (6) increased funding for HS research is crucial to harness these databases' full potential, develop targeted therapies, and ultimately improve patient outcomes. As HS's impact is disproportionate to current research investments, we believe advocating for more resources and addressing database limitations will be key to advancing HS understanding and care.
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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 | 0.225 | 0.473 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.014 | 0.029 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".