Association of mycophenolate and azathioprine use with cognitive function in systemic lupus
Bibliographic record
Abstract
OBJECTIVES: Cognitive dysfunction (CD) is a common manifestation of SLE that can have detrimental consequences for those affected. To date, no treatments have been approved for SLE-CD. This study aims to assess the association of azathioprine (AZA) and mycophenolate (MMF) use with SLE-CD, given that these medications have demonstrated neuroprotective qualities in prior studies. METHODS: Consecutive adult SLE patients presenting to a single healthcare center were considered for participation. The ACR neuropsychological battery for SLE was administered to consenting patients at 0, 6 and 12 months. Scores were compared with age- and sex-matched controls. Primary outcome was CD, defined as a z-score ≤-1.5 in two or more cognitive domains. Mixed-effects logistic regression models were constructed to estimate the odds of CD with respect to AZA and MMF use. RESULTS: A total of 300 participants representing 676 patient visits completed the study; 114 (38%) met criteria for CD at baseline. The cumulative AZA dose (g/kg) was associated with reduced odds of CD [odds ratio (OR) 0.76 (95% CI 0.58, 0.98), P = 0.04]. Years of AZA treatment was also associated with reduced odds of CD [OR 0.72 (95% CI 0.54, 0.97), P = 0.03]. MMF use was not associated with CD. CONCLUSION: AZA use was associated with significantly lower odds of SLE-CD, while MMF use was not. Additional studies are warranted to further investigate the relationship of AZA and SLE-CD.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".