MétaCan
Menu
Back to cohort
Record W3010730211 · doi:10.1097/icu.0000000000000653

The future of retinal imaging

2020· review· en· W3010730211 on OpenAlex
Daniel Q. Li, Netan Choudhry

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCurrent Opinion in Ophthalmology · 2020
Typereview
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsMcGill University
Fundersnot available
KeywordsScanning laser ophthalmoscopyOphthalmoscopyRetinalRetinaOptical coherence tomographyAdaptive opticsFluorescein angiographyComputer scienceSoftware portabilityOptometryOpticsMedicineNeuroscienceOphthalmologyBiologyPhysics

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: This article reviews emerging technologies in retinal imaging, including their scientific background, clinical implications and future directions. RECENT FINDINGS: Fluorescence lifetime imaging ophthalmoscopy is a technology that will reveal biochemical and metabolic changes of the retina at the cellular level. Optical coherence tomography is evolving exponentially toward higher resolution, faster speed, increased portability and more cost effective. Adaptive optics scanning laser ophthalmoscopy fluorescein angiography will provide unprecedented detail of the retinal vasculature down to the level of capillaries, enabling earlier and more sensitive detection of retinal vascular diseases. SUMMARY: Continued developments in retinal imaging focus on improved resolution, faster speed and noninvasiveness, while providing new information on the structure-function relationship of the retina inclusive of metabolic activity at the cellular level.

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.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.995
Threshold uncertainty score0.850

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.060
GPT teacher head0.378
Teacher spread0.318 · 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