P.012 Post-operative hippocampal volume changes on magnetic resonance volumetry in patients with mesial temporal lobe epilepsy
Bibliographic record
Abstract
Background: We evaluate long-term post-operative hippocampal volume (HV) on non-epileptic hippocampus using MR volumetry as well as the neuropsychological outcome in patients with surgery for unilateral mesial temporal lobe epilepsy (MTLE) and achieved seizure-freedom. Methods: We studied 1.5-Tesla MRI before and after epilepsy surgery in 24 patients with MTLE. Serial MRI studies were scheduled at 4 post-operative consecutive periods; 6m-1y; 1-2y; 2-3y; 3-5y. We compared neuropsychological outcomes for memory and estimated IQ at the same periods with serial MRI up to 3 years. Results: The pre-operative non-epileptic HV was significantly smaller than HV in age-matched controls (n=14) (p<0.05). The HV became progressively atrophic after the surgery (p<0.05), correlating with the age at surgery (p<0.05) and pre-operative larger non-epileptic HV (p<0.05), but not with seizure duration. In 14 patients with non-dominant MTLE, the smaller dominant HV at 2-3y period correlated with decline of verbal memory (p<0.05). Conclusions: Post-operative progression of non-epileptic hippocampal atrophy was found with significantly more pronounce in patients with older age at surgery and larger pre-operative non-epileptic hippocampus. After the epileptogenic hippocampus is resected, the remaining hippocampus alone might exhaust to maintain the memory, especially in elders.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".