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Record W3193365345 · doi:10.3389/fnins.2021.709368

TMS Motor Mapping Methodology and Reliability: A Structured Review

2021· review· en· W3193365345 on OpenAlexafffund
Rachel E. Sondergaard, Davide Martino, Zelma H. T. Kiss, Elizabeth G. Condliffe

Bibliographic record

VenueFrontiers in Neuroscience · 2021
Typereview
Languageen
FieldNeuroscience
TopicTranscranial Magnetic Stimulation Studies
Canadian institutionsHotchkiss Brain InstituteUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTranscranial magnetic stimulationComputer scienceReliability (semiconductor)Representation (politics)Motor imagerySelection (genetic algorithm)Artificial intelligencePhysical medicine and rehabilitationPsychologyNeuroscienceStimulationMedicineElectroencephalography

Abstract

fetched live from OpenAlex

Motor cortical representation can be probed non-invasively using a transcranial magnetic stimulation (TMS) technique known as motor mapping. The mapping technique can influence features of the maps because of several controllable elements. Here we review the literature on six key motor mapping parameters, as well as their influence on outcome measures and discuss factors impacting their selection. 132 of 1,587 distinct records were examined in detail and synthesized to form the basis of our review. A summary of mapping parameters, their impact on outcome measures and feasibility considerations are reported to support the design and interpretation of TMS mapping studies.

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)0.001
metaresearch head score (Gemma)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.983
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.194
GPT teacher head0.387
Teacher spread0.192 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations67
Published2021
Admission routes2
Has abstractyes

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