Gender minority patients in dermatology clinical trials
Bibliographic record
Abstract
Patients who identify as part of the lesbian, gay, bisexual, transgender, and queer (LGBTQ+) community often face unique health disparities (Yeung et al., 2019). The term “gender minority” refers to individuals who identify outside of their assigned birth sex, including transgender and gender nonbinary (TGNB) people (Wood and Spach, 2018). The transgender population is a smaller group within the LGBTQ + community who face additional challenges in dermatology, often related to varying stages of transitioning (Sullivan et al., 2019). Accurate estimates of the U.S. TGNB population are unknown. Most population-based surveys do not collect data on gender identity; however, recent studies have reported the prevalence of TGNB identity to be between 0.39% and 2.7% (Nolan et al., 2019). Undoubtedly, the number of individuals self-identifying as TGNB is growing, with particularly higher proportions in young generations. The increase in the TGNB population has been related to greater societal acceptance and awareness of TGNB individuals, which has contributed to increased self-identification (Nolan et al., 2019).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.003 |
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".