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Record W2002919792 · doi:10.1155/2012/687219

Role of Functional MRI in Presurgical Evaluation of Memory Function in Temporal Lobe Epilepsy

2012· article· en· W2002919792 on OpenAlexaff
Chusak Limotai, Seyed M. Mirsattari

Bibliographic record

VenueEpilepsy Research and Treatment · 2012
Typearticle
Languageen
FieldMedicine
TopicEpilepsy research and treatment
Canadian institutionsWestern University
Fundersnot available
KeywordsTemporal lobeMedicineEpilepsyGold standard (test)Epilepsy surgeryWada testCognitive psychologyAudiologyPsychologyRadiologyPsychiatry

Abstract

fetched live from OpenAlex

Many diagnostic tools have been employed to predict the likelihood of a postoperative memory decline after a standard temporal lobectomy, including the intracarotid amobarbital testing (IAT) or Wada, regarded as the gold standard test for over the past half a century. Functional MRI (fMRI) is also a promising tool in that regard. Its routine use to predict the postoperative memory decline has been limited because of the varied study paradigms, discrepancies in analysis, and interpretation of the results. Based on the existing literatures, fMRI cannot replace IAT for the routine presurgical evaluation of the patients with temporal lobe epilepsy (TLE) yet. Large multicentre studies with a panel of memory test are required to determine the full potential of fMRI and use it reliably to replace IAT in the routine clinical practice. In this paper, we review various aspects of memory fMRI, including the experimental designs, data analysis, and findings.

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)0.004
metaresearch head score (Gemma)0.000
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 score0.028
Threshold uncertainty score0.752

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.104
GPT teacher head0.383
Teacher spread0.279 · 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

Citations24
Published2012
Admission routes1
Has abstractyes

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