Correlations of Clusters of Non-Convulsive Seizure and Magnetic Resonance Imaging in a Case With GAD65-Positive Autoimmune Limbic Encephalitis
Bibliographic record
Abstract
With the increased availability of laboratory tests, glutamic acid decarboxylase (GAD) antibody-positive limbic encephalitis has become an emerging diagnosis. The myriad symptoms of limbic encephalitis make the diagnosis challenging. Symptoms range from seizures, memory loss, dementia, confusion, to psychosis. We present a case of a 21-year-old female with GAD65 antibody-positive limbic encephalitis. The case is unique because the clinical course suggests that non-convulsive seizures are the major cause of this patient's clinical manifestations. The following is the thesis: systemic autoimmune disease, associated with the GAD65 antibody, gives rise to seizures, in particular, non-convulsive seizures. Temporal lobes happen to be the most susceptible sites to develop seizures. The greater part of these seizures can be non-convulsive and hard to recognize without electroencephalogram (EEG) monitoring. The variable symptoms mirror the severity and locations of these seizures. The magnetic resonance imaging (MRI) signal abnormities in the bilateral hippocampus, fornix, and mammillary body correlate with the density of these seizures in the similar manner, which suggests it is secondary to post-ictal edema.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".