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Record W2013805562 · doi:10.2478/s13380-013-0116-3

Non-invasive brain stimulation in children: Applications and future directions

2013· article· en· W2013805562 on OpenAlexaff
Thilinie Rajapakse, Adam Kirton

Bibliographic record

VenueTranslational Neuroscience · 2013
Typearticle
Languageen
FieldNeuroscience
TopicTranscranial Magnetic Stimulation Studies
Canadian institutionsAlberta Children's HospitalUniversity of Calgary
FundersNational Institute of Neurological Disorders and Stroke
KeywordsNeurostimulationTranscranial magnetic stimulationNeuromodulationTolerabilityNeuroscienceNeurophysiologyBrain stimulationMedicineNeuroplasticityStimulationPsychologyAlternative medicinePathology

Abstract

fetched live from OpenAlex

Transcranial magnetic stimulation (TMS) is a neurostimulation and neuromodulation technique that has provided over two decades of data in focal, non-invasive brain stimulation based on the principles of electromagnetic induction. Its minimal risk, excellent tolerability and increasingly sophisticated ability to interrogate neurophysiology and plasticity make it an enviable technology for use in pediatric research with future extension into therapeutic trials. While adult trials show promise in using TMS as a novel, non-invasive, non-pharmacologic diagnostic and therapeutic tool in a variety of nervous system disorders, its use in children is only just emerging. TMS represents an exciting advancement to better understand and improve outcomes from disorders of the developing brain.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.816
Threshold uncertainty score0.687

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.018
GPT teacher head0.265
Teacher spread0.246 · 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

Citations106
Published2013
Admission routes1
Has abstractyes

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