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Record W2598070792 · doi:10.1016/j.trci.2017.03.003

Inferior parietal transcranial direct current stimulation with training improves cognition in anomic Alzheimer's disease and frontotemporal dementia

2017· article· en· W2598070792 on OpenAlexaff
Carlos Roncero, Heike Kniefel, Erik Service, Alexander Thiel, Stephan Probst, Howard Chertkow

Bibliographic record

VenueAlzheimer s & Dementia Translational Research & Clinical Interventions · 2017
Typearticle
Languageen
FieldNeuroscience
TopicTranscranial Magnetic Stimulation Studies
Canadian institutionsMcGill UniversityJewish General Hospital
Fundersnot available
KeywordsTranscranial direct-current stimulationStimulationPsychologyNeuropsychologyDementiaBrain stimulationFrontotemporal dementiaNeuroscienceAudiologyPhysical medicine and rehabilitationCognitionMedicineDiseaseInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: We evaluated whether transcranial direct current stimulation (tDCS) can improve picture-naming abilities in subjects with anomic Alzheimer or frontotemporal dementias. METHODS: Using a double-blind crossover design, 10 participants were trained on picture naming over a series of 10 sessions with either 30 minutes of anodal (2 mA) tDCS stimulation to the left inferior parieto-temporal region (P3) or sham stimulation. We evaluated performance on a trained picture-naming list, an equivalent untrained list, and additional neuropsychological tasks. RESULTS: < .01), lasting at least 2 weeks after stimulation. Furthermore, these participants showed a small increase for untrained picture-naming items and digit span when they received real stimulation but a decrease when sham stimulation was received. DISCUSSION: tDCS stimulation has promise as a treatment for anomia in demented individuals and the effect can generalize to unstudied items as well as other cognitive abilities.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.446
GPT teacher head0.502
Teacher spread0.056 · 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 designNon-randomized trial
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

Citations115
Published2017
Admission routes1
Has abstractyes

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