Autoimmune Limbic Encephalitis as an Emerging Pediatric Condition: Case Report and Review of the Literature
Bibliographic record
Abstract
Limbic encephalitis, first described in the 1960s as a paraneoplastic condition, has emerged as an autoimmune condition, occurring often without evidence of an underlying tumor. Many novel autoantibodies have been identified, and this diagnosis is increasingly being made in the pediatric population. This article reports the case of a 16-year-old boy who presented following gastrointestinal illness with subacute evolution of neuropsychiatric symptoms. Brain magnetic resonance imaging revealed progressive hippocampal signal abnormality and swelling. N-methyl-D-aspartate (NMDA) receptor antibody was detected in serum. The patient responded well to pharmacological immunotherapy but has residual cognitive deficits. The available literature on this condition is reviewed. Limbic encephalitis should be considered in the differential diagnosis in children presenting with encephalopathy, particularly with neuropsychiatric manifestations. Long-term surveillance and close follow-up are required to accurately clarify tumor risk and natural history of this condition in children and balance these factors with risks of radiation exposure through imaging.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".