Neuropsychiatric Lupus: Classification Criteria in Neuroimaging Studies
Bibliographic record
Abstract
This systematic review described the criteria and main evaluations methods procedures used to classify neuropsychiatric systemic lupus erythematosus (NPSLE) patients. Also, within the evaluations methods, this review aimed to identify the main contributions of neuropsychological measurements in neuroimaging studies. A search was conducted in PubMed, EMBASE and SCOPUS databases with the terms related to neuropsychiatric syndromes, systemic lupus erythematosus, and neuroimaging techniques. Sixty-six abstracts were found; only 20 were completely analyzed and included. Results indicated that the 1999 American College of Rheumatology (ACR) criteria is the most used to classify NPSLE samples together with laboratorial, cognitive, neurological and psychiatric assessment procedures. However, the recommended ACR assessment procedures to classify NPSLE patients are being used incompletely, especially the neuropsychological batteries. Neuropsychological instruments and neuroimaging techniques have been used mostly to characterize NPSLE samples, instead of contributing to their classifications. The most described syndromes in neuroimaging studies have been seizure/cerebrovascular disease followed by cognitive dysfunctions as well as headache disorder.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.023 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".