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Record W3172249453 · doi:10.1016/j.isci.2021.102727

Binocular visual deficits at mid to high spatial frequency in treated amblyopes

2021· article· en· W3172249453 on OpenAlexafffund
Shijia Chen, Seung Hyun Min, Ziyun Cheng, Yue Xiong, Xi Yu, Lili Wei, Yu Mao, Robert F. Hess, Jiawei Zhou

Bibliographic record

VenueiScience · 2021
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsSpatial frequencyPsychologyNeuroscienceCognitive psychologyOpticsPhysics

Abstract

fetched live from OpenAlex

Amblyopia (lazy eye) is a neurodevelopmental disorder of vision with no ocular pathology. The loss of vision in the amblyopic eye is assumed to be the main deficit in amblyopia, which has resulted in visual acuity (VA) being the primary outcome measure for treatment. Here we used a binocular orientation combination task to quantitatively assess the binocular status by measuring the binocular balance. We set out to determine whether amblyopes who reach the acuity-based end point have a residual binocular imbalance. Our results suggest that even amblyopes who have regained normal acuity have residual binocular deficits over a wide range of spatial frequencies. A further control study suggests that these binocular deficits could not be explained by any residual contrast sensitivity deficits of the amblyopic eye. Consequently, amblyopia is not the primary problem and VA is not the appropriate end point measure.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

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 head0.045
GPT teacher head0.321
Teacher spread0.276 · 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

Citations28
Published2021
Admission routes2
Has abstractyes

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