Neuropsychiatric events in SLE: determination of attribution and assessment of outcome
Bibliographic record
Abstract
Neuropsychiatric (NP) events are common in SLE patients and in the general population. Although some events are a direct result of lupus-related autoimmune and inflammatory mechanisms, many are not. The frequency and diversity of NP events in SLE patients pose a challenge in determining their correct attribution and assessing outcome. These are of fundamental importance for the care of individual patients, studies of pathogenesis and the conduct of clinical trials. A total of 1,826 patients have been enrolled within 15 months of SLE diagnosis into an international, long-term prospective cohort study. Patients are assessed annually for up to 10 years. At each assessment, all NP events as per the ACR case definitions of 19 NP syndromes are recorded. Attribution of each NP event is determined centrally using two composite attribution rules of different stringency. Additional data collection includes demographic and clinical variables, SLE global disease activity (SLEDAI-2K), SLICC/ACR damage index (SDI), physician assessment of NP events (Likert scale) and self-report mental (MCS) and physical (PCS) component summary scores of the SF-36. Statistical analyses include Cox proportional hazards model, Kaplan-Meier curves, regression and multi-state statistical models. Upon enrollment into the cohort the proportion of NP events attributed to SLE varied from 19 to 38% and affected 6.1 to 11.7% of patients, depending upon the stringency of the attribution rule. Seizure disorders, cerebrovascular disease, acute confusional states and neuropathies were the most common of the 19 NP syndromes. Changes in SF-36 summary and subscale scores, in particular those related to mental health, were strongly associated with physician assessment of clinical outcomes of NP events. The short-term outcome of NP events was determined by their characteristics and attribution: focal events and those attributed to SLE had better outcomes. Overall, NP events had a negative impact on self-report health-related quality of life (HRQoL) but this was not necessarily true for all types of NP events. For example, most seizures were attributed to SLE, resolved in the absence of anti-seizure medications and were not associated with a persistent negative impact on HRQoL. The clinical and epidemiological challenge imposed by the high frequency and diversity of NP events in SLE patients can be addressed through the application of composite attribution rules, as well as physician and patient-derived outcome measures.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".