Disparities in the reporting of distribution of health care
Notice bibliographique
Résumé
We read with great interest the article by Wong et al.1 entitled “Ethnic Disparities and Liver Transplantation Rates in Hepatocellular Carcinoma Patients in the Recent Era: Results From the Surveillance, Epidemiology, and End Results Registry.” The authors concluded that ethnic minorities (Hispanics, blacks, and Asians) with hepatocellular carcinoma were significantly less likely to undergo liver transplantation than non-Hispanic whites. They focused on the 4 main ethnic groups and decided to exclude combination ethnicities such as black/Hispanic, Asian/Hispanic, and American Indian/Alaskan Native because the small numbers precluded a precise statistical evaluation. We laud any attempt to reveal disparities in the distribution of health care both in the present era and in the past.2-5 However, we maintain that Asians, blacks, and Hispanics are overly simplistic generic groupings. For example, Hispanics in California and Texas are likely to be of Mexican heritage, whereas Hispanics in Florida are frequently Cuban. In New York, most Hispanics are Puerto Ricans. Each of these subgroups exhibits different medical and social issues in comparison with one another and whites. Asians, who represent the fastest growing ethnic group in the United States, are frequently evaluated as a single homogeneous entity. However, they include Chinese, Filipinos, Indians, Vietnamese, Koreans, Japanese, Pakistanis, Sri Lankans, Nepalese, Cambodians, Thai, Bangladeshis, and Burmese (Myanmarese). Once again, these subgroups frequently display different medical and social issues in comparison with one another and whites. Finally, blacks can be African American, African, Caribbean, European, or Hispanic (as stated in the article) as well as Canadian or Asian. As stated, there are different cardiovascular risk profiles and socioeconomic issues between and among these groups. We understand that it would be extremely tedious to break down each ethnic group into multiple subgroups. However, a careful analysis may have significant ramifications in terms of expectations, center evaluations, and reimbursement. Toward that end, we are currently evaluating the Scientific Registry of Transplant Recipients database for the last 10 years, and we will attempt to analyze ethnic data with these subcategorizations in mind. We hope that this analysis will shed further light on the health care disparities so eloquently demonstrated in this article by Wong et al.1 Afshin Parsikia, M.D., M.P.H. Jorge Ortiz, M.D. Department of Transplant Surgery Einstein Healthcare Network Einstein Medical Center Philadelphia, PA
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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 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; 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 ».