Challenges of Diagnosing Cognitive Dysfunction With Neuropsychiatric Systemic Lupus Erythematosus in Childhood
Bibliographic record
Abstract
The diagnosis of Neuropsychiatric systemic lupus erythematosus disease (NPSLE) is challenging. The Automated Neuropsychological Assessment Metrics (ANAM) has been shown to be an accessible and promising tool for evaluating possible NPSLE in adult and childhood lupus. In this review, we present information about the development and use of Ped-ANAM; the benefit of using Ped-ANAM in children with and without NPSLE in the assessment and follow up of their disease condition; and the correlation of Ped-ANAM to imaging studies such as magnetic resonance imaging (MRI). PedANAM was validated in children with cSLE in different studies. Cognitive performance can be a challenging clinical feature to efficiently assess. However, research with the Ped-ANAM has produced a Cognitive Performance Score (CPS) that allows for a reliable and efficient estimation of cognitive ability and the presence of cognitive limitations that children with cSLE may show. Compared with traditional neurocognitive assessment tools, Ped-ANAM-CPS offers a promising alternative to overcome the difficulties that practitioners previously faced.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".