083. COMPARISON OF ARTERIAL PATTERNS OF DISEASE IN TAKAYASU’S ARTERITIS AND GIANT CELL ARTERITIS
Notice bibliographique
Résumé
Background: Current classification criteria differentiate between Takayasu’s arteritis (TAK) and giant cell arteritis (GCA) based primarily on clinical assessment, yet patients with TAK and GCA may differ in patterns of arterial disease. This study aimed to use computer-based algorithms to determine if patterns of arterial disease are useful to differentiate TAK from GCA with large-vessel involvement (LV- GCA). Methods: Patients with TAK or LV-GCA were studied from the international, Diagnostic and Classification Criteria for Vasculitis (DCVAS) cohort and a combined North America (NA) cohort (Vasculitis Clinical Research Consortium, National Institutes of Health, and Cleveland Clinic). Case inclusion required evidence of large-vessel involvement, defined as stenosis, occlusion, or aneurysm by imaging or catheter-based angiography or ultrasound, or increased FDG uptake by positron-emission tomography (PET) in at least one of 11 specified arterial territories. K-means cluster analysis was performed to identify clusters of patients based on pattern of arterial involvement. Cluster groups were identified in the DCVAS cohort and independently validated in the combined NA cohort. Results: A total of 1,069 were included (DCVAS: TAK=462, GCA=217; NA: TAK=225, GCA=165). Patients with TAK underwent angiography (95%), ultrasonography (28%), or PET imaging (14%). Patients with LV-GCA underwent angiography (50%), ultrasonography (52%), and/or PET imaging (58%). Six distinct clusters of patients were identified in DCVAS and validated in the NA cohort (Figure). Patients in Clusters One, Two, and Three were significantly more likely to have TAK, and patients in Cluster Six were significantly more likely to have LV-GCA. Patients in Clusters Four and Five were equally likely to have TAK or LV-GCA, but assignment in these clusters was driven largely by stenotic disease for TAK and FDG-uptake without stenosis for GCA. Out of all study patients, involvement of the abdominal aorta and renal/mesenteric arteries was the most specific pattern for TAK (134 TAK vs 11 GCA, p < 0.01), while bilateral subclavian/axillary disease was the most specific pattern for GCA (92 GCA vs 23 TAK, p < 0.01). Conclusion: These findings support the incorporation of arterial patterns of disease into classification criteria for large-vessel vasculitis and highlight shared and divergent vascular phenotypes between TAK and GCA. Disclosures: This study was supported by the Intramural Research Program at the National Institute of Arthritis and Musculoskeletal and Skin Diseases. The Vasculitis Clinical Research Consortium (VCRC) is part of the Rare Diseases Clinical Research Network (RDCRN), an initiative of the Office of Rare Diseases Research (ORDR), National Center for Advancing Translational Science (NCATS). The VCRC is funded through collaboration between NCATS, and the National Institute of Arthritis and Musculoskeletal and Skin Diseases (U54 AR057319) and also received funding from the National Center for Research Resources (U54 RR019497).DCVAS is funded by the American College of Rheumatology, European League Against Rheumatism, and the Vasculitis Foundation.
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 | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 ».