Abstract A082: Is there value in gynecologic cancer clinical trial participation for Black women?
Bibliographic record
Abstract
Abstract Purpose Black women are underrepresented in gynecologic cancer clinical trials despite disproportionately worse cancer outcomes. While etiology of clinical trial enrollment disparities is multifactorial, at the individual level, underrepresented populations may be dissuaded from study participation without perceived value. The concept of Return of Value (ROV) describes return of individual research information (both actionable and non-actionable) that participants find most valuable. Ranking of ROV components varies among racial/ethnic groups. In a national survey, Black individuals were more likely to value information on ancestry, genetic traits, future use of their information, and remuneration, compared to White individuals who highly valued information on connections with other study participants and response to medications. Both groups valued information on how genetics affect the risk of getting a medical condition. We evaluated to what extent gynecologic cancer clinical trials include information most valued by Black women to ascertain whether clinical trial design may influence accrual of Black patients. Methods We queried the ClinicalTrials.gov registry for NCI sponsored gynecologic cancer clinical trials in the US between Jan.1994 and Nov.2021. We extracted pre-specified ROV items in basic information, medical record information, research questionnaires (e.g. EORTC QLQ-C30), life-style risk factors (e.g. smoking), ancestry, genetic traits, genetic testing, biomarker testing, imaging, future use of participant information, information about other clinical trials, how to connect with others in the study, and remuneration. We assessed inclusion proportions for each ROV item and assessed temporal changes in these proportions with chi-square tests. Results 279 gynecologic cancer clinical trials were included, with 21% of trials with year of first accrual in 1994-2000, 37% in 2001-2007, 28% in 2008-2014, and 15% in 2015-2021. Most commonly, trials targeted ovarian cancer (48%), were phase II (53%), and utilized chemotherapy (60%) or targeted therapy (34%). Nearly all trials included ROV items in basic information (99%), medical record information (99%), and imaging (82%). 41% of trials included ROV items in biomarker testing, 20% genetic testing, and 20% research questionnaires. Over time, there were significant increases in the proportion of trials that included genetic testing (3% to 51%; p < 0.001) and biomarker testing (14 to 78%, p < 0.001). Information on lifestyle risk factors was rare (1%), and no trials included ROV in ancestry, genetic traits, how to connect with other participants, and remuneration. Conclusion Gynecologic cancer clinical trials include few design elements that provide high value to Black women. In any multi-pronged effort to improve diversity in clinical trial enrollment, inclusion of items valued by Black women should be considered in order to increase enrollment of Black women. This work contributes to the evidence base supporting the importance of person-centered, community-informed clinical trial design. Citation Format: Ann Oluloro, Liz Sage, Elizabeth Swisher, Sarah M. Temkin, Kemi Doll. Is there value in gynecologic cancer clinical trial participation for Black women? [abstract]. In: Proceedings of the 15th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2022 Sep 16-19; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr A082.
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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 | 0.033 | 0.126 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".