Canadian Rheumatology Association Meeting, Victoria, British Columbia, Canada, March 28–31, 2012
Notice bibliographique
Résumé
Objective: Worse outcomes in systemic lupus erythematosus (SLE) patients from lower socioeconomic strata (SES) have been well documented. Low SES is associated with higher chronic stress, which in turn may result in increased inflammation and immune dysfunction. We examined the relationship between SES, autoantibody frequency, and inflammation in SLE patients. Methods: Adult incident and prevalent SLE patients were enrolled in a prospective cohort at a single centre. Sociodemographic variables, data on health-related habits, diagnostic criteria, disease activity, autoantibodies, treatment, and damage were collected annually using standardized tools. SES was measured as educational level achieved and annual household income. Disease activity was evaluated using the Systemic Lupus Activity Measure (SLAM). Organ damage was measured using the SLICC/ACR Damage Index (SDI). Autoantibodies measured included antinuclear antibody, dsDNA antibody, extractable nuclear antigens and anti-phospholipid antibodies. Inflammation was measured using the erythrocyte sedimentation rate (ESR) score from the SLAM. Baseline data was analyzed, testing for differences in ESR score and total number of autoantibodies positive between income and education groups. Significant variables from univariate analyses were then included in multivariate regression models examining for predictors of total autoantibody frequency, ESR score, and organ damage. Results: Two hundred seventy-three patients were enrolled in the cohort, mean disease duration was 13.7 years, and mean age was 48.5 years. Ninety percent were female, 14% had incomes below the poverty line, while 51% had annual incomes >$50,000. Seventy-seven percent had completed high school. No associations were found between SES and autoantibody frequency. Less education and low income were associated with increased ESR scores (p< 0.001, p=0.035 respectively) in univariate analysis. Both income and education were predictors of higher ESR scores in linear regression (p=0.025 and p=0.047 respectively). Higher ESR score and lack of high school completion (p=0.032 and p=0.04 respectively) were predictors of SDI scores when total ACR score, age, and income were included in the regression model.
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 | 0,002 | 0,001 |
| 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) | 0,001 | 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 ».