Reporting of Race and Ethnicity in Randomized Controlled Trials in Hematology
Notice bibliographique
Résumé
Background: Race and ethnicity are important social constructs that are frequently reported in clinical research. Race and ethnicity data can provide insights into the impact of racism, structural inequities, and in some cases ancestry on health outcomes. To ensure that results are generalizable, clinical trials should reflect the diverse populations affected by the disease under investigation. In addition, clear, transparent reporting on how and why race/ethnicity was or was not collected, and a discussion of findings are important. In 2021, JAMA published updated guidance on reporting race and ethnicity as part of the Inclusive Language section of the AMA Manual of Style: A Guide for Authors and Editors as subsection 11.2.3, Race and Ethnicity. We sought to determine the extent to which randomized controlled trials (RCTs) in Hematology, published after these updated guidelines, follow AMA recommendations in this space. Methods: We conducted a systematic review of RCTs of treatments for blood disorders published between April 1, 2022, and June 27, 2024, in 5 high-impact journals: NEJM, JAMA, Lancet, Lancet Hematology, and Blood. The inclusion criteria were: Randomized controlled trials, English language, adult participants, and pertaining to either benign or malignant blood disorders. Data extraction was completed in duplicate with differences resolved by consensus. The following binary outcomes were assessed: 1) Does the methods report whether participant race/ethnicity was collected, 2) Was the rationale for collecting or not collecting race/ethnicity outlined, 3) Was race/ethnicity defined, 4) If race/ethnicity was collected, was it stated who identified the race/ethnicity of participants, 5) Was race/ethnicity of participants reported, and 6) Did the study discuss the implications of race/ethnicity on the findings. Associations between region, journal policies and race/ethnicity reporting were explored in univariate analysis using the chi-squared test for significance. Results: Using MEDLINE, a total of 967 articles were identified. After title and abstract screening, 172 studies were selected for full-text review. Of these, 88 studies were identified for data extraction. For the purposes of this abstract, studies from Jan 1, 2023 to Jun 27, 2024 were included, totaling 61 papers. A minority of methods sections explicitly reported collecting race/ethnicity data (31.1%), why race/ethnicity was or was not collected (6.5%), how race was defined (0%), or who identified the race/ethnicity of participants (29.5%). Most papers (65.6%) reported race/ethnicity data. However, only 26.2% of papers discussed the implications of race/ethnicity with respect to their findings. In univariate analysis, trials recruiting exclusively in Europe were less likely to report race/ethnicity than trials from other regions (p <0.01). Trials published in journals with a formal policy on how and when to report race/ethnicity data had a trend to higher reporting of race/ethnicity (p=0.06) and were more likely to discuss the implications of their findings with respect to race/ethnicity (p=0.04). Conclusion: Efforts to address racial and ethnic disparities in research and healthcare are increasing, with most high-impact studies in this preliminary sample having reported race/ethnicity of participants. Despite this, few trials reported how race/ethnicity was determined or discussed the implications of race/ethnicity with respect to their findings. Regional variation in reporting of race/ethnicity was observed. Formal journal policies may increase adherence to AMA guidance in this domain.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,061 | 0,286 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,006 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».