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Enregistrement W4410715701 · doi:10.3899/jrheum.2025-0390.o043

INTERNATIONAL EFFORT IN HARMONIZING COGNITIVE IMPAIRMENT RESEARCH IN SYSTEMIC LUPUS ERYTHEMATOSUS

2025· article· en· W4410715701 sur OpenAlexaffvenue
Michelle Barraclough, Shane McKie, Letícia Rittner, Andrea Knight, John G. Hanly, Alexandra Legge, Meggan Mackay, Chrisanna Dobrowolski, Hermine I. Brunner, Ekemini A. Ogbu, Chris Wincup, Marcello Govoni, Alessandra Bortoluzzi, Ettore Silvagni, Lesley Ruttan, Kathleen Bingham, Robin Green, Maria Carmela Tartaglia, Susan M. Lee, John D. Fisk, Kâmil Uludaǧ, Christoph M. Tang, Diana Valdés Cabrera, Erik Anderson, Mark DiFrancesco, Alberta Hoi, Sudha Raghunath, Elizabeth Kozora, Xiang He, Rebecca Elliott, Ben Parker, Ian N Bruce, Zahi Touma, Simone Appenzeller

Notice bibliographique

RevueThe Journal of Rheumatology · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueSystemic Lupus Erythematosus Research
Établissements canadiensUniversity of TorontoUniversity Health NetworkNova Scotia Health AuthorityDalhousie UniversityHospital for Sick Children
Organismes subventionnairesnon disponible
Mots-clésMedicineSystemic diseaseCognitionIntensive care medicineCognitive impairmentLupus erythematosusSystemic lupusConnective tissue diseaseImmunologyImmunopathologyInternal medicineAutoimmune diseaseDiseasePsychiatry

Résumé

récupéré en direct d'OpenAlex

O043 / #45 Topic: AS05 - CNS Lupus ABSTRACT CONCURRENT SESSION 07: COGNITION IMPAIRMENT IN SLE – RECENT ADVANCEMENT AND EMERGING RESEARCH 23-05-2025 1:40 PM - 2:40 PM Background/Purpose Cognitive impairment (CI) is frequently observed in systemic lupus erythematosus (SLE) and negatively affects health-related quality of life. Despite increased research in this field, there remains a lack of multicentered studies and external validation of findings between centers. This abstract summarizes the first international, multicenter meeting to discuss the harmonization of research into CI in SLE. The aims of the meeting were to identify the current challenges in this field, knowledge gaps and opportunities to harmonize research across centers. Methods Thirty-seven interdisciplinary researchers (pediatric and adult rheumatologists, psychiatrists, psychologists, physicists, engineers) from 12 centers across 6 countries were invited. Researchers were selected based on having an established publication record in the field of CI in SLE. During the meeting 2 breakout groups were created, one focused on clinical/psychological aspects and the other on neuroimaging. Key topics to discuss were prepared in advance, based on gaps in the current literature and areas that needed further understanding and consistency in research design. These included: core datasets needed for CI in SLE research, current cognitive measures used, weaknesses of these measures, differences between pediatric and adult CI, subjective vs objective CI, current neuroimaging in CI and gaps in the field. Results In terms of a core dataset there was a ‘long-list’ of factors discussed (Table 1). This was not a definitive list but a starting point for additional work required to determine priorities and core factors to consider in CI research. Instead of the American College of Rheumatology neuropsychological battery, identifying specific cognitive domains pertinent to patients with SLE was proposed. This proposed change would help overcome previous test limitations such as validation of tests in alternative languages and in a pediatric population. When considering the new domains, we also need to establish the purpose of the tool, for clinical or research. This setting would then also affect whether we need screening or in-depth measures. Measurements of both objective and subjective CI were considered important, as well as ecologically valid measures to capture cognitive performance in everyday life and measures of resilience. It was agreed that current CI measures account for some potential confounders (eg, age and sex), but other important factors are overlooked, such as social determinants of health. The use of normative data can help with some confounders but regression-based norms maybe more useful. The neuroimaging breakout group identified 10 acquisition methods in current use, with the majority collecting T1 structural, diffusion weighted imaging and resting state functional MRI. The group also identified 15 different types of software that are in use in the analysis stage (Table 2). Discussions included use of the “traveling head” methodology for multicentered studies, and the use of software algorithms in artificial intelligence to computationally harmonize some scans. Overall, in terms of harmonizing imaging research across institutes 4 areas were targeted: scanner features and location, and participant, acquisition protocol, and analytic pipeline harmonization. Table 1: A list of some factors assessed when conducting CI in SLE research Table 2: Imaging data acquisition methods and software currently used by symposium attendees Conclusions Studying CI in SLE is complex and is complicated further by its multifactorial nature and confounders. Minimizing variation by harmonizing research methods, especially clinical and imaging data acquisition and analysis across centers, is an important step to advancing knowledge of CI in the diverse population that is SLE patients. A wider international group will move forward with harmonization efforts and development of a finalized core dataset. Acknowledgment: FAPESP grant 22/00597-6.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,461
score de la tête « metaresearch » (Gemma)0,232
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesMétarecherche
DomaineSignal candidat: Méthodes · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Méthodes · Signal consensuel: aucune
Score de désaccord entre enseignants0,539
Score d'incertitude au seuil0,664

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,4610,232
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0040,005
Bibliométrie0,0100,009
Études des sciences et des technologies0,0030,004
Communication savante0,0130,009
Science ouverte0,0070,028
Intégrité de la recherche0,0060,011
Charge utile insuffisante (le modèle a refusé de juger)0,0060,003

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.

Tête enseignante Opus0,057
Tête enseignante GPT0,370
Écart entre enseignants0,313 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.

Devis d'étudeSans objet
DomaineMéthodes
GenreMéthodes

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission2
Résumé présentoui

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