MétaCan
Menu
Back to cohort
Record W2767676571 · doi:10.3171/2017.5.jns17170

Gamma oscillations in the somatosensory thalamus of a patient with a phantom limb: case report

2017· article· en· W2767676571 on OpenAlexafffund
Diellor Basha, Jonathan O. Dostrovsky, Suneil K. Kalia, Mojgan Hodaie, Andrés M. Lozano, William D. Hutchison

Bibliographic record

VenueJournal of neurosurgery · 2017
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neural Engineering
Canadian institutionsToronto Western HospitalUniversity of TorontoKrembil Foundation
FundersCanadian Institutes of Health Research
KeywordsThalamusSomatosensory systemMicrostimulationMedicinePhantom limbNeurosciencePhantom painEssential tremorDeep brain stimulationImaging phantomThalamotomyAmputationStimulationAnatomyPhysical medicine and rehabilitationPsychologyNuclear medicineSurgeryPathology

Abstract

fetched live from OpenAlex

The amputation of an extremity is commonly followed by phantom sensations that are perceived to originate from the missing limb. The mechanism underlying the generation of these sensations is still not clear although the development of abnormal oscillatory bursting in thalamic neurons may be involved. The theory of thalamocortical dysrhythmia implicates gamma oscillations in phantom pathophysiology although this rhythm has not been previously observed in the phantom limb thalamus. In this study, the authors report the novel observation of widespread 38-Hz gamma oscillatory activity in spike and local field potential recordings obtained from the ventral caudal somatosensory nucleus of the thalamus (Vc) of a phantom limb patient undergoing deep brain stimulation (DBS) surgery. Interestingly, microstimulation near tonically firing cells in the Vc resulted in high-frequency, gamma oscillatory discharges coincident with phantom sensations reported by the patient. Recordings from the somatosensory thalamus of comparator groups (essential tremor and pain) did not reveal the presence of gamma oscillatory activity.

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)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.234
Threshold uncertainty score0.352

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.052
GPT teacher head0.286
Teacher spread0.234 · 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 designCase report
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

Citations5
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueJournal of neurosurgerySame topicNeuroscience and Neural EngineeringFrench-language works237,207