Unmasking Racial Disparity in the Diagnosis and Treatment of Hidradenitis Suppurativa
Bibliographic record
Abstract
Hidradenitis suppurativa (HS) is a chronic, profoundly incapacitating disease predominantly affecting the apocrine gland-rich areas of the human body. Although it affects 0.05% to 4% of the general population, there exists a significant racial disparity, with people of color, particularly Black individuals, experiencing a notably higher prevalence. Despite this disparity, the current literature lacks comprehensive analyses of HS concerning race and ethnicity, revealing a systemic blind spot in understanding and addressing the disease's racially disproportionate impacts. In this commentary, we aim to shed light on these racial disparities, focusing specifically on the stark inequities related to the timely diagnosis and subsequent dermatological care of HS in the United States. This commentary explores the racial bias in HS prevalence, severity, diagnostic delay, access to specialized care, and underrepresentation in clinical trials. By emphasizing the urgent need to address these disparities, we seek to foster an inclusive dialogue and drive proactive efforts toward achieving equitable care and research representation for all populations affected by this debilitating condition. Through this discussion, we aim to pave the way for a healthcare landscape that acknowledges and addresses the racial disparities inherent in HS, ensuring that advancements in the management of the disease cater to the needs of all populations, irrespective of their racial or ethnic background.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".