Lack of Diversity in Orthopaedic Trials Conducted in the United States
Notice bibliographique
Résumé
BACKGROUND: Several orthopaedic studies have suggested patient race and ethnicity to be important predictors of patient functional outcomes. This issue has also been emphasized by federal funding sources. However, the reporting of race and ethnicity has gained little attention in the orthopaedic literature. The objective of this study was to determine the percentage of orthopaedic randomized controlled clinical trials in the United States that included race and ethnicity data and to record the racial and ethnic distribution of patients enrolled in these trials. METHODS: A systematic review of orthopaedic randomized controlled trials published from 2008 to 2011 was performed. The studies were identified through a manual search of thirty-two scientific journals, including all major orthopaedic journals as well as five leading medical journals. Only trials from the United States were included. The publication date, journal impact factor, orthopaedic subspecialty, ZIP code of the primary research site, number of enrolled patients, type of funding, and race and ethnicity of the study population were extracted from the identified studies. RESULTS: A total of 158 randomized controlled trials with 37,625 enrolled patients matched the inclusion criteria. Only thirty-two studies (20.3%) included race or ethnicity with at least one descriptor. Government funding significantly increased the likelihood of reporting these factors (p < 0.05). The percentages of Hispanic and African-American patients were extractable for studies with 7648 and 6591 enrolled patients, respectively. In those studies, 4.6% (352) of the patients were Hispanic and 6.2% (410) were African-American; these proportions were 3.5-fold and twofold lower, respectively, than those represented in the 2010 United States Census. CONCLUSIONS: Few orthopaedic randomized controlled trials performed in the United States reported data on race or ethnicity. Among trials that did report demographic race or ethnicity data, the inclusion of minority patients was substantially lower than would be expected on the basis of census demographics. Failure to represent the true racial diversity may result in decreased generalizability of trial conclusions across clinical populations.
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 | 0,044 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,003 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| 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 | 0,001 |
| 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; un appel candidat d’une seule tête enseignante, 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 ».