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Record W4293382422 · doi:10.1016/j.brs.2022.08.016

A differential effect of visual cortex tDCS on reading of English and Chinese in patients with central vision loss

2022· letter· en· W4293382422 on OpenAlexafffund
Andrew E. Silva, Anqi Lyu, Susan J. Leat, Shamrozé Khan, Tammy Labreche, Jonathan C.H. Chan, Qing Li, George C. Woo, Stanley Woo, Allen M. Y. Cheong, Benjamin Thompson

Bibliographic record

VenueBrain stimulation · 2022
Typeletter
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaVelux Stiftung
KeywordsTranscranial direct-current stimulationPeripheral visionVisual cortexScopusPsychologyDifferential effectsNeuroscienceMedicineStimulationAudiologyPhysical medicine and rehabilitationInternal medicineComputer scienceArtificial intelligenceMEDLINEChemistry

Abstract

fetched live from OpenAlex

Peripheral vision is susceptible to crowding, a difficulty in distinguishing neighbouring objects or features [1]. Crowding impairs face and object recognition and reading in individuals with central vision loss who are forced to rely on peripheral vision [2]. Anodal transcranial direct current stimulation (tDCS) of primary visual cortex reduces crowding in normal peripheral vision [3]. Furthermore, a recent case series suggested that visual cortex tDCS reduces collinear inhibition, a phenomenon related to crowding, in patients with central vision loss due to macular degeneration [4].

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.010
GPT teacher head0.292
Teacher spread0.282 · 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

Citations18
Published2022
Admission routes2
Has abstractyes

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