Nitrated nucleosome levels and neuropsychiatric events in systemic lupus erythematosus; a multi-center retrospective case-control study
Bibliographic record
Abstract
BACKGROUND: In patients with systemic lupus erythematosus (SLE) there is no serological test that will reliably distinguish neuropsychiatric (NP) events due to active SLE from those due to other causes. Previously we showed that serum levels of nitrated nucleosomes (NN) were elevated in a small number of patients with NPSLE. Here we measured serum NN in samples from a larger population of patients with SLE and NP events to see whether elevated serum NN could be a marker for NPSLE. METHODS: We obtained serum samples from patients in the Systemic Lupus International Collaborative Clinics (SLICC) inception cohort. This included 216 patients with NP events and two matched controls with SLE but no NP events for each of these patients. For the NP patients we tested samples taken before, during and after the NP event. RESULTS: Twenty-six patients had events attributed to SLE according to the most stringent SLICC attribution rule. In these patients there was no association between onset of event and elevated serum NN. In 190 patients in whom events were not attributed to SLE by the SLICC rules, median serum NN was elevated at the onset of event (P = 0.006). The predominant clinical features in this group of 190 patients were headache, mood disorders and anxiety. CONCLUSIONS: Serum NN levels rise at the time of an NP event in a proportion of patients with SLE. Further studies are needed to determine the value of serum NN as a biomarker for NPSLE.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".