Racial and ethnic enrollment disparities in clinical trials leading to Food and Drug Administration approvals for gynecologic malignancies
Notice bibliographique
Résumé
BACKGROUND: Compared to White women, Black women and other minority groups have a higher age-adjusted incidence risk of cervical and endometrial cancer. However, the extent of racial and ethnic disparities in clinical trial enrollment among studies performed mainly in North America and Europe for gynecologic malignancy is unknown. OBJECTIVE: This study analyzed enrollment rates by race/ethnicity in trials that led to Food and Drug Administration approvals for gynecological cancers from 2010 to 2024. STUDY DESIGN: This cross-sectional study examined clinical trials registered with ClinicalTrials.gov that resulted in new Food and Drug Administration approvals for gynecologic malignancies between 2010 and 2024. Exclusion criteria were studies not conducted in North America or Europe. Enrollment fractions were obtained by dividing the number of trial participants segregated by the racial/ethnic group by the corresponding U.S. cancer prevalence (uterine, ovarian, and cervical cancer) for 2016 to 2020 for each racial/ethnic group. Odds ratios and 95% confidence intervals were calculated to compare enrollment fractions of minority groups to non-Hispanic Whites. RESULTS: A total of 31 studies met the inclusion criteria, with 21 reporting race/ethnicity data. Three (3/21) studies dichotomized race as non-Hispanic White and non-White and 7 (7/21) reported ethnicity. The median number of participants was 494 [interquartile range 150-674]. Fifteen studies were phase III, and 6 were phase IB/II trials. Treatments included immune checkpoint inhibitors (7 studies), poly (ADP-ribose) polymerase inhibitors (5), vascular endothelial growth factor inhibitors (4), antibody-drug conjugates (4), and an imaging marker (1). Across all studies, 11,258 patients were included, 5563 (49.4%) in ovarian cancer studies, 2963 (26.3%) in endometrial cancer studies, and 2732 (24.3%) in cervical cancer studies. Three studies (n=1734) dichotomized participants into non-Hispanic White and non-White; non-Hispanic White 1291 [74.4%] and non-White 443 [25.6%], and enrollment fractions were 0.51% for non-Hispanic White and 0.43% for no-White, with non-White being underrepresented odds ratio 0.85, 95% confidence interval [0.76-0.95], P=.004. In an Analysis of 18 studies reporting race categories, non-Hispanic Black patients were significantly underrepresented (odds ratio 0.50, 95% confidence interval [0.45-0.54], P<.001), while Asian patients were overrepresented (odds ratio 2.81, 95% confidence interval [2.64-2.99], P<.001). In the 4 studies reporting ethnicity, Hispanic patients were also significantly underrepresented (odds ratio 0.69, 95% confidence interval [0.61-0.78], P<.001). CONCLUSION: In clinical trials, performed in North America and Europe mainly, leading to Food and Drug Administration approvals for gynecologic malignancies, non-Hispanic Black and Hispanic patients are significantly underrepresented compared to non-Hispanic White participants when enrollment is benchmarked to the U.S. female population with gynecological cancer. These trials do not adequately reflect the U.S. populations diagnosed with these malignancies. Enrollment strategies to increase diversity are urgently needed to ensure clinical trial results are equitable and applicable across all populations. Efforts from the American Society of Clinical Oncology, the Association of Community Cancer Centers, and the Gynecologic Oncologic Group/Society of Gynecologic Oncology Inclusion, Diversity, Equity, and Access initiative provide a comprehensive framework for achieving this goal.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,040 | 0,064 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».