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Record W2060425720 · doi:10.3171/jns.2001.95.2.0242

Surgery for temporal lobe epilepsy in older patients

2001· article· en· W2060425720 on OpenAlexaffabout
Warren Boling, F. Andermann, David C. Reutens, François Dubeau, Laetitia Caporicci, André Olivier

Bibliographic record

VenueJournal of neurosurgery · 2001
Typearticle
Languageen
FieldMedicine
TopicEpilepsy research and treatment
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
Fundersnot available
KeywordsMedicineEpilepsyTemporal lobeSurgeryEpilepsy surgeryAnterior temporal lobectomyLesionPediatricsPsychiatry

Abstract

fetched live from OpenAlex

OBJECT: The goal of this study was to evaluate the efficacy of surgery for temporal lobe epilepsy (TLE) in older (> or = 50 years of age) patients. METHODS: The authors conducted a review of all patients 50 years of age 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. In patients aged 50 years and older, the onset of complex partial seizures occurred 5 to 53 years (mean 35 years) prior to the time of surgery. Postoperatively, over a mean follow-up period of 64 months, 15 patients (83%) obtained a meaningful improvement, becoming either free from seizures or only experiencing 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 their ability to achieve freedom from seizures following 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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.422

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.040
GPT teacher head0.311
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations59
Published2001
Admission routes2
Has abstractyes

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