INTERNATIONAL EFFORT IN HARMONIZING COGNITIVE IMPAIRMENT RESEARCH IN SYSTEMIC LUPUS ERYTHEMATOSUS
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.461 | 0.232 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.007 | 0.028 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".