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Record W4383684556 · doi:10.58931/cect.2022.1319

Anterior segment ocular coherence tomography

2022· article· en· W4383684556 on OpenAlexaff
Matthew C. Bujak, Arshdeep Marwaha

Bibliographic record

VenueCanadian Eye Care Today · 2022
Typearticle
Languageen
FieldMedicine
TopicCorneal surgery and disorders
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsOptical coherence tomographyMedicineCorneaOphthalmologyPosterior segment of eyeballCataract surgeryAnterior Eye SegmentOptometryCorneal dystrophy

Abstract

fetched live from OpenAlex

Ocular coherence tomography (OCT) provides non-invasive and rapid in vivo imaging of ocular structures using low coherence interferometry. It first appeared in 1991 for imaging of the posterior segment of the eye; shortly thereafter, the utility of OCT was expanded to the anterior segment (AS-OCT). With improvements in technology including higher resolution and rapid capture speed of images, AS-OCT has become an integral tool for current-day cornea specialists in the clinical evaluation of the cornea and anterior segment. AS-OCT pachymetry is often used to analyze corneal thickness while cross-sectional images assist with the visualization and morphometric analysis of the anterior segment. These features are commonly used to assess endothelial graft attachment and corneal graft health. Though AS-OCT has been used predominately by cornea specialists, it does have wide-spread application for the comprehensive ophthalmology practice. Moreover, the advent of affordable imaging attachment lenses has also made AS-OCT a more practical tool to have in the clinic.
 A comprehensive ophthalmologist can use AS-OCT to monitor pathologies such as recurrent corneal erosions, Salzmann Nodular Degeneration, depth of scarring and endotheliitis. It can also be used in the pre- and post-operative assessment for cataract surgery. For example, AS-OCT can be used to help assess the likelihood of whether a patient with Fuchs’ dystrophy may develop corneal decompensation following cataract surgery. This information can in turn help navigate shared clinical decision making by informing the patient about the risks and benefits of surgery pre-operatively. In the post-operative setting, mild corneal edema is common and expected. However, if there is edema which is out of proportion to either the surgeon’s expectations or the amount of energy from the surgery, a closer look to find the etiology of the edema is warranted. AS-OCT can be used to help delineate common causes of corneal decompensation following cataract surgery including Descemet’s membrane (DM) detachment, retained lens fragments, or infectious causes. We present four clinical scenarios, one of which is the use of AS-OCT in pre-operative assessment and three cases in which AS-OCT is used to identify post-operative complications.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.796
Threshold uncertainty score0.996

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)0.0040.000

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 head0.008
GPT teacher head0.225
Teacher spread0.217 · 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 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
Published2022
Admission routes1
Has abstractyes

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