P.027 Investigation of hippocampal sub-structures in HS and non-HS focal temporal lobe epilepsy at 7T
Bibliographic record
Abstract
Background: The clinical identification of hippocampal sclerosis (HS) is important in predicting surgical outcomes in patients with temporal lobe epilepsy (TLE). In cases where gross hippocampal sclerosis is not identifiable clinically, a more detailed analysis of hippocampal subfields using ultra-high-field magnetic resonance imaging (MRI) may reveal areas of abnormality, which was the focus of our study. Methods: Patients (N=13) with drug-resistant TLE (9 no-HS, 4 HS) and 20 age-matched healthy controls were scanned and compared using a 7T MRI protocol. Using a manual segmentation scheme to delineate hippocampal subfields, subfield-specific volume changes were studied between the two groups. In addition, radiological patient assessment at 7T was correlated with measured subfield changes. Results: Volumetry of the hippocampus at 7T in HS patients revealed significant ipsilateral subfield losses in CA1 and CA4DG. Volumetry also uncovered subfield volume losses in 33% of no-HS patients, which had not been detected conventionally. Furthermore, 89% of no-HS patients showed abnormality (internal architecture or size) at 7T, identified by radiologists blinded to the patient’s initial classification. Conclusions: These preliminary findings indicate that hippocampal subfield volumetry assessed at 7T may be superior to conventional visual inspection by a neuroradiologist in the identification of hippocampal pathologies in TLE.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.007 | 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".