C.1 Cognitive profile in pediatric seronegative autoimmune encephalitis
Bibliographic record
Abstract
Background: Seronegative autoimmune encephalitis (SAE) in children is associated with cognitive deficits, particularly executive dysfunction. However, the relationship between cognitive impairment, disease severity, and lesion burden remains unclear. Identifying these associations could improve patient management and outcomes. This study characterizes neuropsychological symptoms in pediatric SAE and compares patients with and without formal neuropsychological assessments to determine factors influencing cognitive impairment. Methods: A retrospective review was conducted on 155 pediatric autoimmune encephalitis cases, including 80 with SAE. Eleven had neuropsychological evaluations. Statistical analyses assessed differences in age, disease severity, lesion characteristics, hospitalization, and treatment needs. Results: Executive dysfunction was present in 75% of SAE cases. Patients with neuropsychological evaluations were older (median: 8 vs. 3 years, p = 0.0115) and had more severe encephalitis at admission (p = 0.0391) and one year later (p = 0.0011). Lesion burden did not differ (p > 0.05), but patients with assessments had longer hospitalizations and required more intensive treatments (p < 0.005). Conclusions: Executive dysfunction in pediatric SAE is linked to disease severity rather than lesion burden. Systematic neuropsychological assessments should be integrated into patient care. Deeper phenotyping of cognitive profiles and identifying risk factors for poor prognosis will help personalize care in order to improve outcomes.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".