An expert perspective on diversity-oriented standards for assessing sex and gender in clinical research
Bibliographic record
Abstract
Introduction: Randomized controlled trials require diverse patient groups to ensure broad applicability of results. However, gender minorities are often not included, which affects the generalizability and equity of healthcare outcomes. Inclusive research must consider the diversity of sex and gender to eliminate inequalities and improve health outcomes. Methods: A two-stage expert survey was conducted using a self-developed questionnaire in which the constructs of sex, gender, and gender expression were considered. Experts rated the importance and practicality of assessing these concepts in clinical trials and evaluated terms for suitability and comprehension. In addition, existing definitions were refined. Consensus was defined as 70% agreement or disagreement. Results: 14 out of 17 participating experts agreed on the importance to independently assess sex assigned at birth, and 9 out of 16 emphasized this for gender identity in clinical trials. Sex should be assessed with "Please specify your sex assigned at birth" and the answer categories "female", "male", "intersex". Gender identity should be assessed with "I identify as…" and the answer categories "woman", "man", "nonbinary", "trans woman", "trans man", "genderqueer", "genderfluid", "agender", "two spirit". Assessment of gender expression depends on the research question and may not be relevant for every study. Discussion: Our findings emphasize inclusivity by providing multiple gender options and improve data accuracy by allowing individuals to accurately report their gender identity. The results emphasize the importance of distinguishing between sex assigned at birth, gender identity, and gender expression in research. This ensures that gender diversity is accurately represented and considered, improving the relevance and inclusivity of clinical trials.
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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 | 0.680 | 0.661 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.008 | 0.051 |
| Scholarly communication | 0.019 | 0.017 |
| Open science | 0.009 | 0.015 |
| Research integrity | 0.026 | 0.033 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".