Postsurgical seizure outcome in temporal lobe epilepsy patients with normal or subtle, nonspecific MRI findings
Bibliographic record
Abstract
OBJECTIVE: The authors' objective was to report postsurgical seizure outcome of temporal lobe epilepsy (TLE) patients with normal or subtle, nonspecific MRI findings and to identify prognostic factors related to seizure control after surgery. METHODS: This was a retrospective study of patients who underwent surgery from 1999 to 2014 at two comprehensive epilepsy centers. Patients with a clear MRI lesion according to team discussion and consensus were excluded. Presurgical information, surgery details, pathological data, and postsurgical outcomes were retrospectively collected from medical charts. Multiple logistic regression analysis was used to assess the effect of clinical, surgical, and neuroimaging factors on the probability of Engel class I (favorable) versus class II-IV (unfavorable) outcome at last follow-up. RESULTS: The authors included 73 patients (59% were female; median age at surgery 35.9 years) who underwent operations after a median duration of epilepsy of 13 years. The median follow-up after surgery was 30.6 months. At latest follow-up, 44% of patients had Engel class I outcome. Favorable prognostic factors were focal nonmotor aware seizures and unilateral or no spikes on interictal scalp EEG. CONCLUSIONS: Favorable outcome can be achieved in a good proportion of TLE patients with normal or subtle, nonspecific MRI findings, particularly when presurgical investigation suggests a rather circumscribed generator. Presurgical factors such as the presence of focal nonmotor aware seizures and unilateral or no spikes on interictal EEG may indicate a higher probability of seizure freedom.
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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 | 0.001 | 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) | 0.001 | 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".