613 Underrepresentation of Minority Patients in an Observational Cohort Study
Notice bibliographique
Résumé
Background Underrepresentation of ethnic minorities in lupus clinical trials has been identified as an important disparity. We aimed to compare our longitudinal observational lupus cohort participants to our entire lupus clinic population to see if similar disparities exist. Methods All patients seen at our academic centre were entered into a custom database from 1990 until 2015. Diagnoses, demographics, and disease manifestations were recorded. In 2015 this was supplanted by an electronic health record (EHR). In 2002, our centre began enrolling in a longitudinal observational research cohort; all patients meeting 1997 ACR criteria for SLE were eligible. Participation requires formal written consent. All patients with a diagnosis of SLE were abstracted from the database and EHR; only those seen after 2002, when cohort enrolment began, were included in this analysis. Demographics including ethnicity, age at onset, and disease duration and clinical manifestations of SLE were compared between Cohort (Co) and non-Cohort (non-Co) patients. Results 1236 patients were identified; 404 patients were excluded as there were no clinic visits after 2002. Of the remaining 832 patients, 349 (42%) were enrolled in the research cohort, 483 (58%) were not. Age at diagnosis was similar; (Co = 34±14 years vs. non-Co = 36±14 years, p=0.11), while disease duration at last follow-up was longer in Co patients (Co = 17±11 years vs. non-Co = 13±10 years, p<0.001). The ethnic distribution differed between the two groups. Co: White, n= 245 (70.2%); Indigenous n= 58 (16.6%); Asian n = 33 (9.5%); Other n= 13 (3.7%) vs non-Co: White, n= 252 (52.2%); Indigenous n= 162 (33.5%); Asian n = 57 (11.8%); Other n= 12 (2.5%); p<0.001. (figure 1A). Sex distribution was similar: (Co n=29 (8.3%); non-Co n=55 (11.4%), p=0.146). The proportion of patients who had died was higher in non-cohort patients, (Co n= 60 (17.2%); non-Co n= 119 (24.6%), p=0.01); and non-cohort patients were more likely to have died before the age of 50 (Co n=14 (23.3%); non-Co n=44 (37.0%), p=0.07) (figure 1A). Clinical manifestations are shown in figure 1B. While minor mucocutaneous manifestations were more frequent in Co patients, (Malar rash: Co n= 210 (60%); non-Co n= 193 (40%), p<0.001; Photosensitivity: Co n=152 (44%); non-Co n= 146 (30%), p<0.001); Mucosal Ulcerations: Co n= 197 (56%); non-Co n= 129 (28%), p<0.001) there was no difference in renal (Co n= 256 (53%); non-Co n=182 (52%), p=0.81), or neurologic involvement (Co n= 46 (10%); non-Co n= 50 (13%), p=0.21). Conclusions In this single academic centre study, ethnic minority patients were underrepresented in the observational research cohort, mirroring what is described in clinical trial participation. While disease severity (represented by renal and neurologic involvement) did not appear to differ, the higher death rate, and death rate at an early age among nonparticipants suggests underrepresentation of high-risk vulnerable patients in our observational cohort. Observational cohorts represent an important source of real-world data; without representative participation we are lacking data on those lupus patients with the highest prevalence and worst outcomes. Better engagement of ethnic minority and vulnerable patients in research will be key to improve understanding of lupus.
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 | 0,004 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».