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Record W3042113518 · doi:10.5935/0004-2749.20200039

Effects of axial length on retinal nerve fiber layer and macular ganglion cell-inner plexiform layer measured by spectral-domain OCT

2020· article· en· W3042113518 on OpenAlexaff
Carolina L. M. Francisconi, Mário Bernardes Wagner, Roberto Vanin Pinto Ribeiro, André Moraes Freitas

Bibliographic record

VenueArquivos Brasileiros de Oftalmologia · 2020
Typearticle
Languageen
FieldMedicine
TopicGlaucoma and retinal disorders
Canadian institutionsToronto Rehabilitation InstituteUniversity of TorontoDalhousie University
Fundersnot available
KeywordsNerve fiber layerRetinalInner plexiform layerMagnificationOphthalmologyGanglionNerve fiberOptical coherence tomographyGanglion cell layerMedicineAnatomyMaterials scienceOpticsPhysics

Abstract

fetched live from OpenAlex

PURPOSE: To evaluate the influence of ocular axial length on circumpapillary retinal nerve fiber layer and ganglion cell-inner plexiform layer thickness in healthy eyes after correcting for ocular magnification effect. METHODS: In this cross-sectional study, we evaluated 120 eyes from 60 volunteer participants (myopes, emmetropes, and hyperopes). The thickness of the circumpapillary retinal nerve fiber layer and ganglion cell-inner plexiform layer were measured using the spectral optical coherence tomography (OCT)-Cirrus HD-OCT and correlated with ocular axial length. Adjustment for ocular magnification was performed by applying Littmann's formula. RESULTS: Before the adjustment for ocular magnification, age-adjusted mixed models analysis demonstrated a significant negative correlation between axial length and average circumpapillary retinal nerve fiber layer thickness (r=-0.43, p<0.001), inferior circumpapillary retinal nerve fiber layer thickness (r=-0.46, p<0.001), superior circumpapillary retinal nerve fiber layer thickness (r=-0.31, p<0.05), nasal circumpapillary retinal nerve fiber layer thickness (r=-0.35, p<0.001), and average ganglion cell-inner plexiform layer thickness (r=-0.35, p<0.05). However, after correcting for magnification effect, the results were considerably different, revealing only a positive correlation between axial length and temporal retinal nerve fiber layer thickness (r=0.42, p<0.001). Additionally, we demonstrated a positive correlation between axial length and average ganglion cell-inner plexiform layer thickness (r=0.48, p<0.001). All other correlations were not found to be statistically significant. CONCLUSIONS: Before adjustment for ocular magnification, axial length was negatively correlated with circumpapillary retinal nerve fiber layer and ganglion cell-inner plexiform layer thickness measured by Cirrus-OCT. We attributed this effect to ocular magnification associated with greater axial lengths, which was corrected with the Littman's formula. Further studies are required to investigate the impact of ocular magnification correction on the diagnostic accuracy of Cirrus-OCT.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.339
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.015
GPT teacher head0.243
Teacher spread0.229 · 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

Citations27
Published2020
Admission routes1
Has abstractyes

Explore more

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