Timing of early and late seizure recurrence after temporal lobe epilepsy surgery
Bibliographic record
Abstract
PURPOSE: Seizure recurrence after epilepsy surgery has been classified as either early or late depending on the recurrence time after operation. However, time of recurrence is variable and has been arbitrarily defined in the literature. We established a mathematical model for discriminating patients with early or late seizure recurrence, and examined differences between these two groups. METHODS: A historical cohort of 247 consecutive patients treated surgically for temporal lobe epilepsy was identified. In patients who recurred, postoperative time until seizure recurrence was examined using an receiver-operating characteristic (ROC) curve to determine the best cutoff for predicting long-term prognosis, dividing patients in those with early and those with late seizure recurrence. We then compared the groups in terms of a number of clinical, electrophysiologic, and radiologic variables. KEY FINDINGS: Seizures recurred in 107 patients (48.9%). The ROC curve demonstrated that 6 months was the ideal time for predicting long-term surgical outcome with best accuracy, (area under the curve [AUC] = 0.761; sensitivity = 78.8%; specificity = 72.1%). We observed that patients with seizure recurrence during the first 6 months started having seizures at younger age (odds ratio [OR] = 6.03; 95% confidence interval [CI] = 1.06-11.01; p = 0.018), had a worse outcome (OR = 6.85; 95% CI = 2.54-18.52; p = 0.001), needed a higher number of antiepileptic medications (OR = 2.07; 95% CI = 1.16-9.34; p = 0.013), and more frequently had repeat surgery (OR = 9.59; 95% CI = 1.18-77.88; p = 0.021). Patients with late relapse more frequently had seizures associated with trigger events (OR = 9.61; 95% CI = 3.52-26.31; p < 0.01). SIGNIFICANCE: Patients with early or late recurrence of seizures have different characteristics that might reflect diversity in the epileptogenic zone and epileptogenicity itself. These disparities might help explain variable patterns of seizure recurrence after epilepsy surgery.
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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 | 0.001 | 0.006 |
| 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".