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Record W4388719188 · doi:10.1016/j.brs.2023.11.006

Intersession reliability of fast motor mapping using TMS

2023· letter· en· W4388719188 on OpenAlexafffundabout
Faith C. Adams, Stevie D. Foglia, Chloe C. Drapeau, Claudia V. Turco, Karishma R. Ramdeo, Aimee J. Nelson

Bibliographic record

VenueBrain stimulation · 2023
Typeletter
Languageen
FieldNeuroscience
TopicTranscranial Magnetic Stimulation Studies
Canadian institutionsUniversity of AlbertaMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsReliability (semiconductor)Reliability engineeringComputer scienceNeurosciencePsychologyEngineeringPhysics

Abstract

fetched live from OpenAlex

The organization of the primary motor cortex (M1) can be non-invasively assessed using transcranial magnetic stimulation (TMS) combined with frameless neuro-navigation. By delivering single pulses of TMS over M1 and recording the motor evoked potentials (MEP) from target muscles, the location and size of muscle representations can be obtained to create a “motor map” [1]. Characteristics such as area, volume, and center of gravity (CoG) are quantified from motor maps. Motor mapping typically requires upwards of 30 minutes to complete which reduces its utility in time-sensitive contexts.

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)0.004
metaresearch head score (Gemma)0.031
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: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.031
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.108
GPT teacher head0.327
Teacher spread0.219 · 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

Citations1
Published2023
Admission routes3
Has abstractyes

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