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Record W4387968281 · doi:10.1097/opx.0000000000002081

Refractive Index Measurement of the Crystalline Lens <i>in Vivo</i>

2023· article· en· W4387968281 on OpenAlexaff
Ji C. He

Bibliographic record

VenueOptometry and Vision Science · 2023
Typearticle
Languageen
FieldMedicine
TopicOphthalmology and Visual Impairment Studies
Canadian institutionsCegep de Sept Iles
FundersNational Eye InstituteNational Institutes of Health
KeywordsScheimpflug principleLens (geology)OpticsRefractive indexOptical coherence tomographyGradient-index opticsHuman eyeMaterials scienceCorneaPhysics

Abstract

fetched live from OpenAlex

SIGNIFICANCE: This study provides a new method to measure the refractive index of crystalline lens in the human eye in vivo . PURPOSE: Accessing the refractive index of crystalline lenses in the human eye in vivo has long been a challenge. This study aimed to measure the refractive index of a lens in vivo using an anterior segment optical coherence tomography (AS-OCT) system combined with a Scheimpflug imaging system. METHOD: A ray-traceable Scheimpflug imaging was developed and integrated into an AS-OCT system. A theoretical study revealed that the combination of these two systems provides a unique solution for simultaneously measuring the refractive index and the thickness of the lens. The average lens refractive index along the ray pathway ( Nav ) and lens shape were measured for seven subjects. RESULTS: The lens Nav along the central ray varies from 1.383 to 1.419 between subjects. The lens refractive index function across the lens diameter varies from subject to subject. The lens Nav increases for accommodated eyes. The thicknesses and profiles of the lenses are also determined. CONCLUSIONS: The lens refractive index varies substantially from individual eye to individual eye, not only along the central ray pathway but also for the lens refractive index function across the lens diameter. Ray-traceable Scheimpflug imaging-equipped AS-OCT is useful for testing the refractive index of lenses in the human eye in vivo .

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.117
Threshold uncertainty score0.212

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.051
GPT teacher head0.460
Teacher spread0.409 · 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 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
Published2023
Admission routes1
Has abstractyes

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