P47 Cognitive impairment in jSLE – The role of inflammation
Bibliographic record
Abstract
<h3>Background</h3> Patients with juvenile-onset Systemic Lupus Erythematosus (jSLE) cope with physical and neuropsychiatric symptoms which may interfere with social and professional activities. Mild cognitive impairment may negatively impact quality of life and academic performance. We intend to assess cognitive state in JSLE and to explore laboratory and clinical markers associated with the presence of mild cognitive impairment. <h3>Methods</h3> Thirty jSLE patients, currently aged ≥16 years, followed in an outpatient’s unit performed the Mini Mental State Examination (MMSE) for cognitive testing; clinical and laboratory measures were collected from clinical records. Juvenile-onset was defined as age at diagnosis <18 years. Statistical analyses were performed with SPSS software, version 25. <h3>Results</h3> The mean age was 22.8(5.3) years with 90% females. Patients had a mean of 12.7(2.3) years of formal education. A mean MMSE of 27.7 (1.9) was found, and 23.3% of the population showed mild cognitive impairment. MMSE scores were negatively correlated with C-Reactive Protein (CRP) (r=-0.38, p=0.44) and platelet count (r=-0.37, p=0.44). Additionally, patients presenting positive anti-SSA (n=10), anti-RNP (n=4) and anti-SM (n=4) autoantibodies, scored significantly lower MMSE scores compared to patients without these autoantibodies (p=0.029; p=0.017 and p=0.017, respectively). <h3>Conclusions</h3> Our population showed a low mean MMSE score, regardless of their educational level and age. The consistent relationship between the presence of cognitive impairment, higher inflammatory activity and autoantibodies frequently associated with neuropsychiatric involvement in jSLE seems to point to the need to screen jSLE populations for mild cognitive impairment with larger studies being required.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.003 | 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".