Understanding the evolving treatment landscape of hidradenitis suppurativa: An analysis of All of Us
Bibliographic record
Abstract
BACKGROUND: Hidradenitis suppurativa (HS) is a chronic skin condition with significant burden for affected patients. The development of new therapies in the last decade has brought hope for better patient outcomes, but understanding the use of these new drug classes is necessary to ensure patient access to care. METHODS: We analysed data from 2636 HS patients within NIH's All of Us research program from June 2017 and October 1, 2023 to examine the likelihood of patients receiving certain classes of drugs based on socio-demographic factors and comorbid health conditions, as well as how these drug classes impact quality of life. We also examined trends in the number of prescriptions over time. RESULTS: Antibiotics were most frequently prescribed, with higher numbers administered to females. Small molecule inhibitors and biologic medications were prescribed at low levels. We found that socio-demographic factors such as ethnicity, income and insurance provider influence the types of drugs patients are most likely to receive, with African American individuals more likely to receive antibiotics and immunosuppressive drugs and less likely to receive small molecule inhibitors. We also found that comorbid conditions significantly influence the likelihood of patients receiving specific drugs, with higher odds of receiving biologics if a patient has a comorbid rheumatic/autoimmune disorder. Antibiotics and immunosuppressive drugs were associated with less favourable quality of life measures, as well as a higher likelihood of anxiety and depression. CONCLUSIONS: This work uncovers the varied landscape of HS treatment in the US and highlights the factors that influence treatment choices and how these treatments impact quality of life. It also provides an understanding of the social disparities that some populations face when accessing HS care, informing future decisions and practices to reduce these inequities.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".