Hippocampal Abnormalities in an MR Imaging Series of Patients with Tuberous Sclerosis
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Hippocampal abnormalities are known as highly epileptogenic precursor lesions in the general population, primarily manifesting as MTS. The purpose of this study was to evaluate the occurrence of hippocampal abnormalities on MR imaging in patients with TS to explore the possible underlying mechanisms of the abnormalities and to identify the relationship between an abnormal HF and epilepsy. MATERIALS AND METHODS: We studied MR images and clinical data from 31 patients with TS. The MR imaging protocol was identical for all patients and included tilted coronal images of their temporal lobes. The diagnosis of TSC was made according to established criteria. The HFs of the patients were evaluated from coronal images according to size, morphology, and signal intensity. The data were submitted to statistical analysis, and P values < or = .05 were considered significant. RESULTS: We found HF abnormalities in 5 patients. Four had typical MTS, and 1 had HIMAL. We found a positive correlation between childhood febrile seizures and MTS in patients with TS. We also confirmed that patients with altered hippocampi had a tendency to exhibit more cortical tubers. CONCLUSIONS: Analysis of this series of patients demonstrated the presence of HF abnormalities, mainly MTS in patients with TS. We also found that the occurrence of febrile seizures during the first year of life appears to be one of the determining factors for MTS development in these patients.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".