How to optimize and evaluate diversity in gynecologic cancer clinical trials: statements from the GCIG Barcelona Meeting
Bibliographic record
Abstract
Findings from clinical trials have led to advancement of care for patients with gynecologic malignancies. However, restrictive inclusion of patients into trials has been widely criticized for inadequate representation of the real-world population. Ideally, patients enrolled in clinical trials should represent a broader population to enhance external validity and facilitate translation of outcomes across all relevant groups. Specifically, there has been a systematic lack of data for underrepresented groups, with many studies failing to report or differentiate study participants based on sociodemographic domains, such as race and ethnicity. As such, the impact of treatment in these underrepresented groups is poorly understood, and clinical outcomes according to various sociodemographic factors are infrequently assessed. Inclusion of diverse trial participants, with different racial and ethnic background, is essential for the understanding of factors that may impact clinical outcomes. Therefore, we conducted a multi-national meeting of clinical trial groups and industry with the goal of increasing equity, diversity, and inclusion in gynecologic cancer clinical trials and to address barriers to recruitment, participation, and harmonization of data collection and reporting. These Gynecologic Cancer Intergroup (GCIG) statements present recommendations and strategies for the gynecologic cancer research community to improve equity, diversity, and inclusion in gynecologic cancer 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 | 0.629 | 0.547 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.022 | 0.014 |
| Open science | 0.011 | 0.022 |
| Research integrity | 0.057 | 0.048 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".