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The role of suppression in amblyopia

2011· article· en· W1995137908 on OpenAlexaff
Jinghua Li, Benjamin Thompson, CS Lam, Lily Y. L. Chan, Goro Maehara, George C. Woo, Minbin Yu, RF Hess

Bibliographic record

VenueActa Ophthalmologica · 2011
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsMcGill University
Fundersnot available
KeywordsAnisometropiaStereoscopic acuityStrabismusOptometryMedicineDegree (music)OphthalmologyAudiologyVisual acuityRefractive errorPhysics

Abstract

fetched live from OpenAlex

Abstract Purpose This study had three main aims; to assess the degree of suppression in patients with strabismic, anisometropic and mixed amblyopia, to establish the relationship between suppression and the degree of amblyopia and to compare the degree of suppression across the clinical sub‐groups within our sample. Methods Using both standard measures of suppression (Bagolini lenses and ND filters, Worth 4 dots) and a new approach involving the measurement of dichoptic motion thresholds under conditions of variable interocular contrast, we quantified the degree of suppression in 43 amblyopic patients with strabismus, anisometropia or a combination of both. Results There was good agreement between the quantitative measures of suppression made using the new dichoptic motion threshold technique and measurements made using standard clinical techniques (Bagolini lenses and ND filters, Worth 4 dots). The degree of suppression was found to directly correlate with the degree of amblyopia within our clinical sample whereby stronger suppression was associated with a greater interocular acuity difference and poorer stereoacuity. Suppression was not related to the type or angle of strabismus when this was present or the previous treatment history. Conclusion These results suggest that suppression may have a primary role in the amblyopia syndrome and therefore have implications for the treatment of 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 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)0.000
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 score0.068
Threshold uncertainty score0.784

Codex and Gemma teacher scores by category

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 head0.101
GPT teacher head0.327
Teacher spread0.226 · 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

Citations14
Published2011
Admission routes1
Has abstractyes

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