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Record W3048461031 · doi:10.1167/iovs.61.10.21

Reflexive Fusional Vergence and Its Plasticity Are Impaired in Convergence Insufficiency

2020· article· en· W3048461031 on OpenAlexaff
Ian M. Erkelens, William R. Bobier

Bibliographic record

VenueInvestigative Ophthalmology & Visual Science · 2020
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsVergence (optics)Convergence insufficiencyConvergence (economics)Divergence (linguistics)ReflexivityMedicineAudiologyOphthalmologyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Purpose: We compared the adaptive capacities of reflexive fusional convergence and divergence in 10 participants with untreated convergence insufficiency (CI) to 10 age-matched binocularly normal controls (BNCs) in an effort to elucidate the functional basis of CI. Methods: Vergence responses were monitored binocularly at 250 Hz using video-based infrared oculography, while single and double-step disparity stimuli were viewed dichoptically. The double-step stimuli were designed to induce an adaptive increase in the convergence or divergence reflexive fusional response dynamics. Results: As expected, convergence responses in the CI population were significantly slower at baseline (BNC 12.0 ± 1.8°/s vs. CI 7.4 ± 2.5°/s; P < 0.001), but divergence response velocities were similar between groups (P = 0.38). Critically, we observed an impaired adaptive change in convergence peak velocities in the CI group when compared to BNCs (-18.2% ± 27.3% vs. 25.4% ± 9.8%; P < 0.001). Adaptive changes in reflexive fusional divergence responses were similar between groups (P > 0.5) and significantly less robust when compared to BNC convergence. Conclusions: The results support the hypothesis that the adaptive capacities of vergence are related to the strength of the underlying reflexive fusional response. Combined, the evidence suggests that the clinical condition of convergence insufficiency is underpinned by an underdeveloped or perturbated reflexive fusional vergence response mechanism. We relate these observations to different clinical guidelines for the management and treatment of this condition.

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 categoriesMeta-epidemiology (narrow), Science and technology studies
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 score0.131
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0010.004
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.137
GPT teacher head0.376
Teacher spread0.239 · 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.

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
Published2020
Admission routes1
Has abstractyes

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