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Record W4242256536 · doi:10.4103/2348-0548.199942

Intraoperative electrocorticography

2017· article· en· W4242256536 on OpenAlexaff
Gabriela Alcaráz, Pirjo Manninen

Bibliographic record

VenueJournal of Neuroanaesthesiology and Critical Care · 2017
Typearticle
Languageen
FieldMedicine
TopicEpilepsy research and treatment
Canadian institutionsToronto Western HospitalUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsElectrocorticographyMedicineAwake craniotomyEpilepsy surgeryCraniotomyAnestheticAnesthesiaEpilepsySurgery

Abstract

fetched live from OpenAlex

Abstract Intraoperative electrocorticography (ECoG) is the recording of electrophysiological activity from electrodes placed directly on the exposed surface of brain, during surgery for epilepsy and tumor resection. The ECoG is helpful in defining the seizure onset and spread within the cortical surface and delineation of the interface between epileptogenic zones and functional cortex substance of the brain. Intraoperative ECoG is an invasive procedure, it is performed during surgery mostly commonly during awake craniotomy but at times during general anaesthesia. As most anesthetic agents will affect ECoG, they should be minimized or stopped prior to any recording. Activation of intraoperative epileptiform activity may also be required if there are no spontaneous discharges. The appropriate management of the anesthetic during the time of ECoG is critical for its success. There are limitations and some controversies to all the uses of intraoperative ECoG, thus each center will set their own indications, criteria, and protocols.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.003

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 head0.027
GPT teacher head0.362
Teacher spread0.335 · 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 source (direct Gemma or distilled Codex), 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

Citations7
Published2017
Admission routes1
Has abstractyes

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