Abstract 1290: Racial and ethnic representation and disparities on clinical guideline panels in oncology
Notice bibliographique
Résumé
Abstract Purpose: Culturally competent diverse workforce is critical to equitable and progressive cancer care. Yet, racial bias, permeates practice and research in oncology. The National Comprehensive Cancer Network (NCCN) panels recommend guidelines that determine standards for patient care in the United States (US). We investigated the extent of racial/ethnic representation/disparity amid this higher echelon of cancer leadership. Methods: We collected data from publicly available NCCN (https://www.nccn.org) guidelines (version: 1.2023-5.2023) published between 11/2022-11/2023. Six research team members extracted data. Race/ethnicity (NIH categories: White, Black, Hispanic/Latinx, Asian) was identified by advanced AI detection software tools (Namsor and Kairos, utilizing names and facial recognition to infer US race). Gender was determined using name/pronoun (if available). All information was confirmed using online databases (institutional profiles, biographical paragraphs, social media) and group consensus (in case of discrepancies). Data regarding active US physicians was obtained using Association of American Medical Colleges (AAMC) 2022 physician specialty data report. The primary objective was to determine the racial/ethnic composition of panels and association between race/ethnicity and chairs/co-chairs/vice-chairs (lead positions or leads) within panels. Descriptive statistics were used. Proportions were compared using Fisher-exact or Chi-squared test (odds-ratio [OR] and 95% confidence intervals [95%CI] were reported). Results: We reviewed 63 panels corresponding to 63 distinct disease sites. A total of 1223 unique individuals [475 (38.8%) females and 748 (61.2%) males] accounted for 2162 panel members with a median of 34 members (range: 25-42) per panel. Racial/ethnic representation was 1455 (67.3%) Whites, 570 (26.4%) Asians, 93 (4.3%) Hispanics/Latinx, and 44 (2.0%) Blacks, which was comparable to racial/ethnic makeup of active US physicians (64%, 21%, 7% and 6%, respectively). Among these 2162 member positions, 129 (6.0%) were lead positions. In these leads, racial/ethnic representation was 105 (81.4%) Whites, 5 (11.4%) Blacks, 17 (3.0%) Asians, and 2 (2.2%) Hispanics/Latinx. Notably, no significant difference was seen between proportion of female (5.2%) and male (6.4%) leads (OR: 0.80; 95%CI:0.55-1.16; P=0.26). However, the proportion of Whites in lead positions was significantly higher than Non-whites (7.2% vs. 3.4%; OR: 2.2; 95%CI: 1.4-3.5; P < 0.001). Conclusions: Although, overall membership of NCCN panels shows favorable racial/ethnic diversity, underrepresentation and bias appear to subsist for non-White race/ethnicity, when it comes to leadership positions within this key decision-making body that influences cancer care. Further efforts to improve this disparity within oncology is an essential step to shaping equity within the field. Citation Format: Jonathan M. Loree, Arvind Dasari, Mena Shaheed, Himanish Gothwal, Kulwinder Singh, Hewad Shaheed, Riya Mangal, Shivek Gothwal, Jason Willis, Michael J. Overman, Scott Kopetz, Kanwal Pratap Singh Raghav. Racial and ethnic representation and disparities on clinical guideline panels in oncology [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1290.
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 | 0,009 | 0,064 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,014 | 0,001 |
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 ».