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Record W2039316630 · doi:10.1155/2012/201651

Temporal Lobe Epilepsy Surgery Failures: A Review

2012· review· en· W2039316630 on OpenAlexaff
Adil Harroud, Alain Bouthillier, Alexander G. Weil, Dang Khoa Nguyen

Bibliographic record

VenueEpilepsy Research and Treatment · 2012
Typereview
Languageen
FieldMedicine
TopicEpilepsy research and treatment
Canadian institutionsUniversité de MontréalHôpital Notre-Dame
Fundersnot available
KeywordsTemporal lobeEpilepsyAnterior temporal lobectomyMedicineEpilepsy surgeryLesionMesial temporal lobe epilepsySurgeryPsychiatry

Abstract

fetched live from OpenAlex

Patients with temporal lobe epilepsy (TLE) are refractory to antiepileptic drugs in about 30% of cases. Surgical treatment has been shown to be beneficial for the selected patients but fails to provide a seizure-free outcome in 20-30% of TLE patients. Several reasons have been identified to explain these surgical failures. This paper will address the five most common causes of TLE surgery failure (a) insufficient resection of epileptogenic mesial temporal structures, (b) relapse on the contralateral mesial temporal lobe, (c) lateral temporal neocortical epilepsy, (d) coexistence of mesial temporal sclerosis and a neocortical lesion (dual pathology); and (e) extratemporal lobe epilepsy mimicking TLE or temporal plus epilepsy. Persistence of epileptogenic mesial structures in the posterior temporal region and failure to distinguish mesial and lateral temporal epilepsy are possible causes of seizure persistence after TLE surgery. In cases of dual pathology, failure to identify a subtle mesial temporal sclerosis or regions of cortical microdysgenesis is a likely explanation for some surgical failures. Extratemporal epilepsy syndromes masquerading as or coexistent with TLE result in incomplete resection of the epileptogenic zone and seizure relapse after surgery. In particular, the insula may be an important cause of surgical failure in patients with TLE.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.900
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0070.002
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.003

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 head0.258
GPT teacher head0.450
Teacher spread0.192 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreReview

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

Citations146
Published2012
Admission routes1
Has abstractyes

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