Disparities in the Enrollment of Racialized, Ethnic Minority, and Older Adults in Randomized Trials of Acute Myeloid Leukemia: A Systematic Review
Notice bibliographique
Résumé
Introduction: There are significant racial and ethnic disparities in incidence and survival for patients diagnosed with hematologic malignancies. Understanding discrepancies in enrollment in randomized controlled trials based on race, ethnicity, and age is important to better understand access to care and clinical trial conduct. Objective: To systematically review the literature on enrollment of racialized, ethnic minority, and older adults in randomized controlled trials (RCTs) of acute myeloid leukemia (AML) and to provide enrollment estimates and compare these to population characteristics. Methods: MEDLINE was searched from inception through to June 2023. No restrictions based on language or publication date were used. Pairs of reviewers independently screened titles, abstracts, and full texts of records. Inclusion criteria were phase II and III RCTs of disease modifying therapy for AML in adults (≥18 years) reporting efficacy and safety outcomes. We excluded single arm trials, trials with unpublished results, conference abstracts, follow-up reports, subgroup/post-hoc/exploratory analyses, and supportive care trials, except those evaluating disease-modifying therapies and reporting clinical efficacy. A standardized form was pilot-tested and used to extract data related to trial characteristics. The proportion of trials reporting racial and ethnic subgroups (African American/Black, Asian, American Indian/Alaskan native, Native Hawaiian or other Pacific Islander, White, Hispanic), and age ≥65 were determined. For US trials, we calculated the enrollment incidence ratios (EIRs), the ratio of trial proportions of members of a racial and ethnic subgroup divided by US population-based incidence in the corresponding racial and ethnic subgroup using the Surveillance, Epidemiology, and End Results (SEER 20) database. We conducted a random-effects meta-analysis to pool EIRs. Results: After screening 7,759 titles and abstracts and 157 full texts, we included 90 studies, of which 14 (15%) were US trials. Overall, there were 21 (23.3%) trials that reported race or ethnicity and 26 (28.9%) that reported the enrollment proportion of ≥65 years. Of the trials with data on race or ethnicity, 15 (71.4%) had data on African American/Blacks, 21 (100%) Whites, 14 (66.7%) Asian or Pacific Islanders, 2 (9.52%) American Indian and Alaskan Native, 4 (19.0%) Hispanics. Of trials reporting on race, 176 (3.6%) of participants were African American/Black, 360 (7.2%) Asian or Pacific Islander, 5 (0.1%) American Indian and Alaskan Native, 3,914 (79.6%) White, and 39 (0.8%) Hispanic. Of the 14 US trials, 4 (28.6%) reported race and 4 (28.6%) reported enrolled proportion of older adults. Hispanic patients (EIR 0.20; 95%CI 0.07 to 0.59, I 2=86%), and Asian patients (EIR 0.24; 95%CI 0.07 to 0.86, I 2=77%) were significantly underrepresented while White patients (EIR 1.33, 95%CI 1.07 to 1.66, I 2=99%) were significantly overrepresented. Confidence intervals were wide for EIR of Black patients (EIR 0.96, 95%CI 0.42 to 2.2, I2=91%). Conclusion: Most trials did not report data on race and ethnicity or on enrollment proportion of participants ≥65 years. Only a small proportion of trial participants were from racial and ethnic minority groups. Hispanic and Asian patients were significantly underrepresented while White patients were overrepresented.
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 | 0,049 | 0,211 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,014 | 0,010 |
| Bibliométrie | 0,012 | 0,013 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».