Cluster Analysis of an Array of Autoantibodies in Neuropsychiatric Systemic Lupus Erythematosus
Bibliographic record
Abstract
To the Editor: Neuropsychiatric symptoms in patients with systemic lupus erythematosus (SLE) present a challenge to the clinician because they can be caused by the underlying disease (neuropsychiatric SLE; NPSLE) or coexist independently1. No specific diagnostic test is available for NPSLE. Reports on the associations between specific antinuclear autoantibodies and distinct NPSLE syndromes have been conflicting2,3,4,5, perhaps because of the laboratory tests used to detect these autoantibodies. New multiplex technologies for the detection of autoantibodies have emerged in the last years and might be helpful in diagnosing NPSLE. We hypothesized that a cluster of autoantibodies could be associated with a specific NPSLE syndrome or with focal or diffuse NPSLE manifestations. Therefore we used an addressable laser bead immunoassay test in patients who visited the NPSLE clinic in Leiden, the Netherlands, a tertiary referral center for patients with SLE who have neuropsychiatric symptoms. Between September 2007 and February 2012, 133 patients with SLE who had neuropsychiatric symptoms were evaluated and diagnosed consecutively by a multidisciplinary team6. All patients fulfilled the revised SLE criteria of the American College … Address correspondence to Dr. E.J.M. Zirkzee, Department of Rheumatology, Leiden University Medical Center, PO Box 9600, 2300 RC Leiden, The Netherlands. E-mail: E.J.M.Zirkzee{at}LUMC.nl
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".