Bibliographic record
Abstract
Surgery for Temporal Lobe Epilepsy in Older Patients Boling W, Andermann F, Reutens D, Dubeau F, Caporicci L, Olivier A J Neurosurg 2001;95:242–248 Objective The goal of this study was to evaluate the efficacy of surgery for temporal lobe epilepsy (TLE) in older (older than 50 years) patients. Methods The authors conducted a review of all patients aged 50 years or older with TLE surgically treated at the Montreal Neurological Institute and Hospital since 1981 by one surgeon (A.O.). Only patients without a mass lesion were included. Outcome parameters were compared with those of younger individuals with TLE, who were stratified by age at operation. Results In patients aged 50 years and older, the onset of complex partial seizures occurred 5 to 53 years (mean, 35 years) before the time of surgery. Postoperatively, over a mean follow-up period of 64 months, 15 (83%) patients obtained a meaningful improvement, becoming either free from seizures or experiencing only a rare seizure. Most surgery outcomes were similar in both older and younger individuals, except for a trend to more freedom from seizures and increased likelihood of returning to work or usual activities in the younger patients. Note that a patient's long-standing seizure disorder did not negatively affect the ability to achieve freedom from seizures after surgery. Conclusions Surgery for TLE appears to be effective for older individuals, comparing favorably with results in younger age groups, and carries a small risk of postoperative complications.
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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 | 0.001 |
| 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".