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Record W2620180033 · doi:10.1186/s40942-017-0081-z

Choroidal vascular analysis in myopic eyes: evidence of foveal medium vessel layer thinning

2017· article· en· W2620180033 on OpenAlexaff
Rayan A. Alshareef, Mohammed Khuthaila, Manideepak Januwada, Abhilash Goud, Daniela Ferrara, Jay Chhablani

Bibliographic record

VenueInternational Journal of Retina and Vitreous · 2017
Typearticle
Languageen
FieldMedicine
TopicRetinal Diseases and Treatments
Canadian institutionsMcGill University
Fundersnot available
KeywordsOphthalmologyChoroidThinningMedicineFovealOptical coherence tomographyPathologicalRetinalRetinaOpticsPathologyBiology

Abstract

fetched live from OpenAlex

PURPOSE: To analyse morphologic features of the choroid in Non-pathological myopic eyes using spectral-domain (SD) optical coherence tomography (OCT). METHODS: ) from fovea. Medium choroidal vessel thickness (MCVT) was calculated by subtracting LCVT from CT. Choriocapillaris was encompassed by MCVT, given its reduced thickness. Linear regression analysis evaluated the relationship between age and axial with CT, LCVT and MCVT. RESULTS: The study group comprised 42 eyes of 31 patients (mean age 46.13 ± 15.63; 15 females). Control group included 57 eyes of 34 patients (mean age of 42.3 ± 15.29; 24 females). Mean axial length in myopic eyes and control group was 26.57 ± 1.27 and 23.59 ± 0.99 mm respectively. Myopic eyes showed significant thinning of MCVT and CT at all locations (p < 0.0001) compared to controls, unlike LCVT (p > 0.05). With each decade, thinning of up to 37 µm in CT was noted along with thinning of LCVT (up to 22.6 µm) and MCVT (up to 25 µm). Each mm increase in axial length caused 38.2 µm thinning of choroid along with LCVT (<10 µm), however, MCVT showed more notable thinning (>30 µm). CONCLUSION: Significant thinning of MCVT was noted in non-pathological myopic eyes in comparison to healthy subjects. It appears that MCVT has stronger relationship with age and axial length.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.307

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.028
GPT teacher head0.368
Teacher spread0.340 · 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

Citations49
Published2017
Admission routes1
Has abstractyes

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