Factors predictive of suboptimal seizure control following selective amygdalohippocampectomy
Bibliographic record
Abstract
OBJECT: Selective amygdalohippocampectomy (SelAH) is used in the treatment of mesial temporal lobe epilepsy (MTLE). The goal of this study was to determine factors predictive of poor postoperative seizure control (Engel Class III or IV) following SelAH. METHODS: A retrospective study was conducted of 27 patients with poor seizure control postoperatively (Engel III/IV group), in comparison with 27 patients who were free from seizures after surgery (Engel I/II group). The results of electroencephalography, magnetic resonance (MR) imaging, and pathological studies were reviewed, and volumetric MR image analysis was used to compare the extent of the mesial structures that had been resected. In 56% of patients in the Engel III/IV group, significant bitemporal abnormalities were displayed on preoperative EEG studies, compared with 24% of patients in the Engel I/II group (p < 0.05). An analysis of preoperative MR images disclosed five patients (19%) in the Engel III/IV group and no patient in the Engel I/II group with normal hippocampal volumes bilaterally. Thirteen patients in the Engel III/IV group subsequently underwent either extension of the SelAH (six cases) or a corticoamygdalohippocampectomy (seven patients). Three patients from the former and one patient from the latter subgroup subsequently became seizure free (four patients total [34%]). The remaining nine patients did not improve despite the fact that they had undergone near-total resection of mesial structures. CONCLUSIONS: The majority of patients receiving suboptimal seizure control following SelAH did not meet the criteria for unilateral MTLE, based on EEG, MR imaging, and/or histopathological studies. These patients were therefore unlikely to benefit from additional resection of mesial structures. With the benefits of modern imaging, and by strict adherence to selection criteria, SelAH can be predicted to yield excellent postoperative seizure control for nearly all patients with unilateral MTLE. There remains a subpopulation, however, that meets the criteria for MTLE, but does not become free from seizure following SelAH.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".