The Role of Sex and Gender in Dermatology - From Pathogenesis to Clinical Implications
Bibliographic record
Abstract
BACKGROUND: Sex and gender have increasingly been recognized as significant risk factors for many diseases, including dermatological conditions. Historically, sex and gender have often been grouped together as a single risk factor in the scientific literature. However, both may have a distinct impact on disease incidence, prevalence, clinical presentation, severity, therapeutic response, and associated psychological distress. OBJECTIVES AND PROJECT DESCRIPTION: The mechanisms that underlie differences in skin diseases between males, females, men, and women remain largely unknown. The specific objectives of this review paper are:To highlight the biological differences between males and females (sex), as well as the sociocultural differences between men and women (gender) and how they impact the integumentary system.To perform a literature review to identify important sex- and gender-related epidemiological and clinical differences for various skin conditions belonging to a range of disease categories and to discuss possible biological and sociocultural factors that could explain the observed differences.To discuss dermatological skin conditions and gender-affirming treatments within the transgender community, a population of individuals who have a gender identity which is different than the gender identity they were assigned at birth. FUTURE IMPACT: With the rising number of individuals that identify as non-binary or transgender within our increasingly diverse communities, it is imperative to recognize gender identity, gender, and sex as distinct entities. By doing so, clinicians will be able to better risk-stratify their patients and select treatments that are most aligned with their values. To our knowledge, very few studies have separated sex and gender as two distinct risk factors within the dermatology literature. Our article also has the potential to help guide future prevention strategies that are patient-tailored rather than using a universal approach.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".