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Record W4282838840 · doi:10.1016/j.isci.2022.104598

Modulation of mean luminance improves binocular balance across spatial frequencies in amblyopia

2022· article· en· W4282838840 on OpenAlexafffund
Seung Hyun Min, Yu Mao, Shijia Chen, Robert F. Hess, Jiawei Zhou

Bibliographic record

VenueiScience · 2022
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsMcGill University
FundersScience Fund for Distinguished Young Scholars of Zhejiang ProvinceCanadian Institutes of Health ResearchNational Natural Science Foundation of ChinaNational Key Research and Development Program of ChinaWenzhou Medical University
KeywordsMonocularBinocular visionLuminanceBalance (ability)Spatial frequencyAccommodationOptometryOpticsMedicinePhysicsPhysical medicine and rehabilitation

Abstract

fetched live from OpenAlex

Amblyopia is a visual impairment that perturbs binocular balance at high spatial frequencies in favor of the fellow eye. Studies reveal that amblyopes who had been treated with monocular therapies still show imbalance. Binocular balance is achieved when both eyes' inputs are weighed equally. A reduced light can diminish the dimmed eye's weight in binocular combination. In this study, we examined if binocular balance across spatial frequencies could be improved by reducing the luminance of the fellow eye in adult amblyopes. By doing so, we relieved their binocular imbalance across spatial frequencies. Also, normal observers showed amblyopic binocular imbalance when the dominant eye's light level was dimmed. Therefore, reducing the luminance in the unaffected eye in amblyopia mitigated the binocular imbalance, whereas doing so in normal adults simulated the amblyopic imbalance across spatial frequencies.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.176
Threshold uncertainty score0.405

Codex and Gemma teacher scores by category

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

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 head0.041
GPT teacher head0.318
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations9
Published2022
Admission routes2
Has abstractyes

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