Validation of the Brief Cognitive Symptoms Index in Sjögren Syndrome
Bibliographic record
Abstract
OBJECTIVE: The Brief Cognitive Symptoms Inventory (BCSI) is a short, self-report scale designed to measure cognitive symptomatology in patients with rheumatic disease. To facilitate research and clinical practice, we tested the internal consistency and validity of the BCSI in patients with Sjögren syndrome (SS). METHODS: Patients who met the American-European Consensus Group criteria for SS and healthy controls completed a questionnaire assessing symptoms including cognitive complaints. We calculated Cronbach's alpha to assess internal consistency and Pearson correlation coefficients to test for association between BCSI, symptoms, and demographic variables. Total score distribution was analyzed to establish cutoff criteria for differentiation of case versus non-case. We compared neuropsychological outcomes of patients with SS above and below the threshold BCSI score to assess the association of cognitive symptoms with objective cognitive deficits. RESULTS: Complete data were available on 144 patients with SS and 35 controls. Internal consistency of the BCSI was good. Scores were similar in all patient groups and patients reported more cognitive symptoms than controls (p < 0.0001). BCSI scores correlated moderately with pain, depression, anxiety, fatigue, and health quality. High scores for cognitive dysfunction were reported by 20% of the patients with SS and only 3% of controls. Patients with cognitive scores > 50 had more depression, fatigue, pain (effect size all > 1), and worse performance on multiple cognitive domains. CONCLUSION: The BCSI should be a useful tool for the study of cognitive symptoms in SS. Both self-report and standardized tests should be considered in screening for cognitive disorders in SS.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".