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Record W2647259881 · doi:10.1016/j.optom.2017.02.003

Relationship between vessel diameter and depth measurements within the limbus using ultra-high resolution optical coherence tomography

2017· article· en· W2647259881 on OpenAlexafffund
Emmanuel Alabi, Natalie Hutchings, Kostadinka Bizheva, Trefford Simpson

Bibliographic record

VenueJournal of Optometry · 2017
Typearticle
Languageen
FieldMedicine
TopicGlaucoma and retinal disorders
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOptical coherence tomographyMedicineOphthalmologyAnatomyNuclear medicine

Abstract

fetched live from OpenAlex

To establish a relationship between the diameter and depth position of vessels in the superior and inferior corneo-scleral limbus using ultra-high resolution optical coherence tomography (UHR-OCT). Volumetric OCT images of the superior and inferior limbus were acquired from 14 healthy subjects with a research-grade UHR-OCT system. Differences in vessel diameter and depth between superior and inferior limbus were analyzed using repeated measured ANOVA in SPSS and R. The mean (± SD) superior and inferior diameters were 29 ± 18 μm and 24 ± 18 μm respectively, and the mean (± SD) superior and inferior depths were 177 ± 109 μm and 207 ± 132 μm respectively. The superior limbal vessels were larger than the inferior ones (RM-ANOVA, p = 0.004), and the inferior limbal vessels were deeper than the superior vessels (RM-ANOVA, p = 0.041). There was a positive linear association between limbal vessel depth and size within the superior and inferior limbus with Pearson correlation coefficients of 0.803 and 0.754, respectively. This study demonstrated that the UHR-OCT was capable of imaging morphometric characteristics such as the size and depth of vessels in the limbus. The results of this study suggest a difference in the size and depth of vessels across different positions of the limbus, which may be indicative of adaptations to chronic hypoxia caused by the covering of the superior limbus by the upper eyelid. UHR-OCT may be a useful tool to evaluate the effect of contact lenses on the microvascular properties within the limbus. Establecer la relación entre el diámetro y la profundidad de los vasos del limbo esclerocorneal superior e inferior mediante tomografía de coherencia óptica de ultra-alta resolución (UHR-OCT). Se adquirieron 256 conjuntos de imágenes del limbo superior e inferior en 14 sujetos, mediante UHR-OCT. Se analizaron las diferencias en cuanto a diámetro y profundidad del vaso entre el limbo superior e inferior utilizando ANOVA de medidas repetidas en SPSS y R. Los diámetros medios (± DE) superior e inferior fueron de 29 μm ± 18 μm y 24 μm ± 18 μm respectivamente, y las profundidades medias (± DE) superior e inferior fueron de 177 μm ± 109 μm y 207 μm ± 132 μm respectivamente. Los vasos del limbo superior fueron de mayor tamaño que los del limbo inferior (RM-ANOVA, p = 0,004), y los vasos del limbo inferior fueron más profundos que los del limbo superior (RM-ANOVA, p = 0,041). Se produjo una asociación lineal positiva entre la profundidad y el tamaño del vaso dentro del limbo superior e inferior, con coeficientes de correlación de Pearson de 0,803 y 0,754, respectivamente. Este estudio demuestra que UHR-OCT fue capaz de obtener imágenes de las características morfométricas tales como tamaño y profundidad de los vasos del limbo. Los resultados de este estudio sugieren una diferencia de tamaño y profundidad de los vasos en las diferentes posiciones del limbo, que puede ser indicativa de adaptaciones a la hipoxia crónica causada por el cubrimiento del limbo superior por parte del párpado superior. UHR-OCT puede ser una herramienta de utilidad para evaluar el efecto de las lentes de contacto sobre las propiedades microvasculares del limbo esclerocorneal.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.078
GPT teacher head0.355
Teacher spread0.277 · 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 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".

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Citations3
Published2017
Admission routes2
Has abstractyes

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