Temporal Lobectomy in Early Childhood: The Need for Long-Term Follow-Up
Bibliographic record
Abstract
We retrospectively identified 15 children ages 12 years and under with anticonvulsant resistant epilepsy who underwent a temporal lobectomy at Children's Hospital, Boston, between 1978 and 1993. Our aim was to study the long-term seizure outcome. Data pertaining to preoperative evaluation, electroencephalography (EEG), neuroimaging, surgery, seizure outcome, and postoperative complications were reviewed. Only patients followed for more than 12 months were included. The average duration of follow-up was 57 months. At the last visit, 47% (7 of 15) of the children were seizure free or only had auras: another 33% (5 of 15) had > 90% reduction in seizure frequency. Three patients had < 90% seizure reduction. Four cases were initially seizure free but had subsequent recurrence between 11 and 28 months after the epilepsy surgery. Factors associated with a good outcome include exclusively focal EEG discharges or an imaging suggestive of a low-grade tumor; factors associated with a poor outcome include generalized EEG discharges and a normal magnetic resonance image. Temporal lobectomy is useful in the treatment of early childhood drug-resistant partial epilepsy, but long-term follow-up is necessary as late seizure recurrence may occur up to 28 months after 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".