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Record W4281257089 · doi:10.1016/j.clae.2022.101716

Multifocal contact lens vision simulated with a clinical binocular simulator

2022· article· en· W4281257089 on OpenAlexfundno aff
Xoana Barcala, María Viñas, Sofía Ruíz, Fernando José Alcantarilla Hidalgo, Derek Nankivil, Tom Karkkainen, Enrique Gambra, Carlos Dorronsoro, Susana Marcos

Bibliographic record

VenueContact Lens and Anterior Eye · 2022
Typearticle
Languageen
FieldMedicine
TopicOphthalmology and Visual Impairment Studies
Canadian institutionsnot available
FundersHORIZON EUROPE Excellent ScienceResearch to Prevent BlindnessCanadian Institute of Steel ConstructionHorizon 2020 Framework ProgrammeNational Eye InstituteCenter for Visual ScienceJohnson and Johnson Vision CareInstituto de Salud Carlos III
KeywordsBinocular visionContact lensOptometryOpticsLens (geology)Computer scienceSimulationPsychologyComputer visionMedicinePhysics

Abstract

fetched live from OpenAlex

PURPOSE: The purpose of this study is to compare the binocular visual perception of participants wearing multifocal contact lenses and these same lens designs viewed through a temporal multiplexing visual simulator. METHODS: Visual performance and perceived visual quality at various distances were obtained in 37 participants wearing soft M-CLs and through the SimVis Gekko programmed with the same lenses. In a pilot study (n = 10) visual performance was measured in terms of LogMAR visual acuity (VA) at far (4 m), intermediate (64 cm) and near (40 cm) distances and through-focus VA (TFVA) curves with the simulated M-CLs. In the follow-up study (n = 27), LogMAR VA at far, intermediate and near distances were measured both with the actual and simulated M-CLs. Perceived visual quality was measured in both studies using the Multifocal Acceptance Score (MAS-2EV), and a Participants Reported Outcomes Vision questionnaire. Differences between the metrics obtained with simulated and actual lenses were obtained. RESULTS: Both actual and simulated M-CLs increased depth-of-focus by a similar amount. Mean LogMAR VA differences with actual and simulated M-CLs ranged between 4 and 6 letters (0.08 ± 0.01, 0.12 ± 0.01 and 0.10 ± 0.01, for far, intermediate and near distances, respectively). MAS-2EV average score differences with actual and simulated M-CLs ranged between -1.00 and + 4.25. Average MAS-2EV scores were not correlated significantly with VA. However, MAS-2EV (average and individual scores) were highly correlated to visual quality questionnaire responses (p < 0.005). CONCLUSIONS: A simultaneous vision simulator accurately represented vision with M-CLs both VA at various distances and perceived visual quality, as measured in a clinical setting. The MAS-2EV metric accurately captured participant reported outcomes of standard vision questionnaires. The combination of SimVis Gekko and MAS-2EV has the potential to largely reduce chair time in M-CLs fitting.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.036
GPT teacher head0.377
Teacher spread0.341 · 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 designSimulation or modeling
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

Citations16
Published2022
Admission routes1
Has abstractno

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