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Record W4392015281 · doi:10.4103/sjopt.sjopt_176_23

Optical coherence tomography imaging of the peripheral retina

2024· article· en· W4392015281 on OpenAlexaff
Niveditha Pattathil, Netan Choudhry

Bibliographic record

VenueSaudi Journal of Ophthalmology · 2024
Typearticle
Languageen
FieldMedicine
TopicRetinal Diseases and Treatments
Canadian institutionsUniversity of TorontoOctane (Canada)
Fundersnot available
KeywordsOptical coherence tomographyMedicineRetinaTomographyOptometryOphthalmologyOpticsRadiologyPhysics

Abstract

fetched live from OpenAlex

This review critically assesses the applicability of retinal periphery imaging technology, scrutinizing its practical limitations and potential advancements within ophthalmology. It underscores the significant costs and the need to evaluate the clinical utility of optical coherence tomography (OCT) and OCT angiography devices. It emphasizes how clinicians should consider their practice-specific use-cases while investing in devices with capabilities like ultra-widefield OCT, autofluorescence imaging, and angiography. The paper also highlights the challenges associated with image acquisition, such as artifact management and patient cooperation for extended fixation periods. This review outlines the utility of these modalities in various retinal pathologies, as well as their contribution to telemedicine and personalized care, facilitated by artificial intelligence for improved image processing, quantification, and interpretation. These techniques potentially offer a more comprehensive understanding of peripheral retinal conditions and associated pathologies, thus influencing clinical decision-making, particularly in remote regions with limited specialist access.

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 score0.098
Threshold uncertainty score0.513

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 head0.016
GPT teacher head0.313
Teacher spread0.297 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

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