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Record W2008553101 · doi:10.1080/13554790590944663

Improved naming after TMS treatments in a chronic, global aphasia patient – case report

2005· article· en· W2008553101 on OpenAlexaff
Margaret A. Naeser, Paula I. Martin, Marjorie Nicholas, Errol Baker, Heidi Seekins, Nancy Helm‐Estabrooks, Carol Cayer-Meade, Masahito Kobayashi, Hugo Théoret, Felipe Fregni, Josep M. Tormos, Jacquie Kurland, Karl W. Doron, Álvaro Pascual‐Leone

Bibliographic record

VenueNeurocase · 2005
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
FundersNational Center for Research ResourcesNational Institutes of HealthNational Institute on Deafness and Other Communication DisordersU.S. Department of Veterans Affairs
KeywordsTranscranial magnetic stimulationAphasiaPsychologyLateralization of brain functionAudiologyLesionBasal gangliaMedicinePhysical medicine and rehabilitationNeuroscienceStimulationPsychiatryCentral nervous system

Abstract

fetched live from OpenAlex

We report improved ability to name pictures at 2 and 8 months after repetitive transcranial magnetic stimulation (rTMS) treatments to the pars triangularis portion of right Broca's homologue in a 57 year-old woman with severe nonfluent/global aphasia (6.5 years post left basal ganglia bleed, subcortical lesion). TMS was applied at 1 Hz, 20 minutes a day, 10 days, over a two-week period. She received no speech therapy during the study. One year after her TMS treatments, she entered speech therapy with continued improvement. TMS may have modulated activity in the remaining left and right hemisphere neural network for naming.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.292
Teacher spread0.277 · 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 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

Citations177
Published2005
Admission routes1
Has abstractyes

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