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Record W2313726208 · doi:10.1227/neu.0b013e31822b30cd

Neuromodulation in Epilepsy

2011· review· en· W2313726208 on OpenAlex
Faisal Alotaibi, Clement Hamani, Andrés M. Lozano

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNeurosurgery · 2011
Typereview
Languageen
FieldNeuroscience
TopicVagus Nerve Stimulation Research
Canadian institutionsToronto Western Hospital
Fundersnot available
KeywordsNeuromodulationMedicineVagus nerve stimulationEpilepsyNeuroscienceNeurostimulationDeep brain stimulationSubthalamic nucleusStimulationThalamusBrain stimulationVagus nerveParkinson's diseasePsychologyInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

Neuromodulation strategies have been proposed to treat a variety of neurological disorders, including medication-resistant epilepsy. Electrical stimulation of both central and peripheral nervous systems has emerged as a possible alternative for patients who are not deemed to be good candidates for resective procedures. In addition to well-established treatments such as vagus nerve stimulation, epilepsy centers around the world are investigating the safety and efficacy of neurostimulation at different brain targets, including the hippocampus, thalamus, and subthalamic nucleus. Also promising are the preliminary results of responsive neuromodulation studies, which involve the delivery of stimulation to the brain in response to detected epileptiform or preepileptiform activity. In addition to electrical stimulation, novel therapeutic methods that may open new horizons in the management of epilepsy include transcranial magnetic stimulation, focal drug delivery, cellular transplantation, and gene therapy. We review the current strategies and future applications of neuromodulation in epilepsy.

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.

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)0.000
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.996
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.302
GPT teacher head0.402
Teacher spread0.100 · 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