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Record W2086984547 · doi:10.7490/f1000research.1558.1

Quantitative measurement of interocular suppression in children with amblyopia

2011· article· en· W2086984547 on OpenAlexaff
Sathyasri Narasimhan, Emily R. Harrison, Deborah Giaschi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsContrast (vision)AudiologyOpen peer reviewInterference (communication)MedicineOptometryPsychologyPlant biologyOphthalmologyOpticsPhysicsComputer scienceBiology

Abstract

fetched live from OpenAlex

In this study we explored the possibility of using a dichoptic global motion technique to measure interocular suppression in children with amblyopia. We compared children (5-16 years old) with unilateral anisometropic and/or strabismic amblyopia to age-matched control children. Under dichoptic viewing conditions, contrast interference thresholds were determined with a global motion direction-discrimination task. Using virtual reality goggles, high contrast signal dots were presented to the amblyopic eye, while low contrast noise dots were presented to the non-amblyopic fellow eye. The contrast of the noise dots was increased until discrimination of the motion direction of the signal dots reached chance performance. Contrast interference thresholds were significantly lower in the strabismic group than in the anisometropic and control group. Our results suggest that interocular suppression is stronger in strabismic than in anisometropic amblyopia.

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)0.002
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.147
GPT teacher head0.321
Teacher spread0.175 · 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

Citations1
Published2011
Admission routes1
Has abstractyes

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