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Record W3202307365 · doi:10.1016/j.isci.2021.103652

A clinically convenient test to measure binocular balance across spatial frequency in amblyopia

2021· article· en· W3202307365 on OpenAlexafffund
Seung Hyun Min, Yu Mao, Shijia Chen, Zhifen He, Robert F. Hess, Jiawei Zhou

Bibliographic record

VenueiScience · 2021
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsMcGill University
FundersCanadian Institutes of Health ResearchNational Key Research and Development Program of ChinaMedical Science and Technology Project of Zhejiang ProvinceScience Fund for Distinguished Young Scholars of Zhejiang ProvinceNational Natural Science Foundation of ChinaNatural Science Foundation for Distinguished Young Scholars of Anhui Province
KeywordsBalance (ability)Binocular visionRepeatabilityTest (biology)Reliability (semiconductor)Measure (data warehouse)OptometryVisual acuityComputer scienceStereoscopic acuityAudiologyPsychologyArtificial intelligenceMedicineOphthalmologyNeuroscienceMathematicsStatisticsPhysics

Abstract

fetched live from OpenAlex

Amblyopia is a visual disorder that originates from the brain. It exhibits no pathology in the eye. Studies have shown that measuring both visual acuity and binocular balance for assessing amblyopia could be more helpful. However, tests that measure binocular balance are time-consuming, often exceeding 30 min. Their long test durations prevent them from being used in the clinic. For this reason, we have developed a quick (i.e., about 7 min) and precise tool that quantitatively measures binocular balance of patients with amblyopia. The new test can capture binocular imbalance that is typically exhibited at high spatial frequency in amblyopes. In addition, it has an excellent test-retest reliability and repeatability between two experimental sessions. We hope that our newly developed test can pave the road for physicians and researchers to better assess and diagnose amblyopia and other visual disorders that disrupt binocular balance beyond the laboratory.

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)0.005
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 score0.080
Threshold uncertainty score0.794

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.077
GPT teacher head0.372
Teacher spread0.295 · 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

Citations12
Published2021
Admission routes2
Has abstractyes

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