Self-reported cognitive dysfunction and memory impairment in Systemic Autoimmune Rheumatic Diseases (SARDs): a mixed methods analysis of the INSPIRE cohort
Bibliographic record
Abstract
OBJECTIVES: To explore self-reported cognitive dysfunction, including memory impairment, across systemic autoimmune rheumatic diseases (SARDs) and examine its impact and associations with demographic, clinical and psychosocial factors. METHODS: A mixed-methods approach was employed, surveying 1853 SARD patients and 463 controls using validated instruments including the Everyday Memory Questionnaire-Revised (EMQ-R). Kruskal-Wallis tests and Spearman's rank correlations were used to compare the groups. Additionally, 67 in-depth interviews were conducted for qualitative thematic analysis. RESULTS: Systemic lupus erythematosus (SLE), undifferentiated connective tissue disease (UCTD) and Sjögren's patients reported significantly higher rates of memory impairments than other groups. There was no evidence of increased self-reported memory impairment with disease duration or age. Moderate positive associations were found between EMQ scores and the lifetime frequency of all other neuropsychiatric symptoms. EMQ-R was positively associated with self-assessment of overall disease activity (r = 0.291, P < 0.001) and negatively correlated with well-being (r = -0.397, R2 = 0.159). Expanding on the quantitative findings, qualitative analyses highlighted the adverse impact of cognitive dysfunction on daily participation in activities, social isolation, self-esteem and mental well-being, and the potential underreporting of these symptoms to clinicians. CONCLUSION: This study highlights a significant impairment of memory in SARDs, notably in SLE, UCTD and Sjögren's, and the impact of cognitive impairment on daily lives and well-being. The positive associations with disease activity and neuropsychiatric symptoms, and negative association with well-being emphasizes the need for targeted interventions. Future research should prioritize developing pharmacological and psychosocial interventions to address cognitive dysfunction in SARD patients. While reassuringly there was no evidence of worsening memory impairment over time, the underreporting of symptoms also suggests that cognitive issues may be more prevalent than clinical records indicate and thus emphasize the importance of thorough patient assessment.
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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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".