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Record W4212821702 · doi:10.1016/j.oret.2022.02.007

Recommendations for OCT Angiography Reporting in Retinal Vascular Disease

2022· article· en· W4212821702 on OpenAlexaff
Marion R. Munk, Amir H. Kashani, Ramin Tadayoni, Jean‐François Korobelnik, Sebastián Wolf, Francesco Pichi, Adrian Koh, Akihiro Ishibazawa, Alain Gaudric, Anat Loewenstein, Bruno Lumbroso, Daniela Ferrara, David Sarraf, David T. Wong, Dimitra Skondra, Francisco J. Rodríguez, Giovanni Staurenghi, Ian Pearce, Judy E. Kim, K. Bailey Freund, Maurízio Battaglia Parodi, Nadia K. Waheed, Richard B. Rosen, Richard F. Spaide, Shintaro Nakao, Srinivas R. Sadda, Stela Vujosevic, Tien Yin Wong, Toshinori Murata, Usha Chakravarthy, Yuichiro Ogura, Wolfgang Huf, Meng Tian

Bibliographic record

VenueOphthalmology Retina · 2022
Typearticle
Languageen
FieldMedicine
TopicRetinal Diseases and Treatments
Canadian institutionsUniversity of TorontoUniversity Health Network
FundersNational Institute of Neurological Disorders and StrokeNational Eye InstituteNational Institutes of HealthHeidelberg EngineeringCarl Zeiss Meditec AGAllerganApellis PharmaceuticalsGenentechBayer
KeywordsMedicineOphthalmologyRetinalAngiographyFluorescein angiographyOptometryRadiology

Abstract

fetched live from OpenAlex

PURPOSE: To develop a consensus nomenclature for reporting OCT angiography (OCTA) findings in retinal vascular disease (e.g., diabetic retinopathy, retinal vein occlusion) by international experts. DESIGN: Delphi-based survey. SUBJECTS, PARTICIPANTS, AND/OR CONTROLS: Twenty-five retinal vascular disease and OCTA imaging experts. METHODS, INTERVENTION, OR TESTING: A Delphi method of consensus development was used, comprising 2 rounds of online questionnaires, followed by a face-to-face meeting conducted virtually. Twenty-five experts in retinal vascular disease and retinal OCTA imaging were selected to constitute the OCTA Nomenclature in Delphi Study Group for retinal vascular disease. The 4 main areas of consensus were: definition of the parameters of "wide-field (WF)" OCTA, measurement of decreased vascular flow on conventional and WF-OCTA, nomenclature of OCTA findings, and OCTA in retinal vascular disease management and staging. The study end point was defined by the degree of consensus for each question: "strong consensus" was defined as ≥85% agreement, "consensus" as 80% to 84%, and "near consensus" as 70% to 79%. MAIN OUTCOME MEASURES: Consensus and near consensus on OCTA nomenclature in retinal vascular disease. RESULTS: A consensus was reached that a meaningful change in percentage of flow on WF-OCTA imaging should be an increase or decrease ≥30% of the absolute imaged area of flow signal and that a "large area" of WF-OCTA reduced flow signal should also be defined as ≥30% of the absolute imaged area. The presence of new vessels and intraretinal microvascular abnormalities, the foveal avascular zone parameters, the presence and amount of "no-flow areas," and the assessment of vessel density in various retinal layers should be added for the staging and classification of diabetic retinopathy. Decreased flow ≥30% of the absolute imaged area should define an ischemic central retinal vein occlusion. Several other items did not meet consensus requirements or were rejected in the final discussion round. CONCLUSIONS: This study provides international consensus recommendations for reporting OCTA findings in retinal vascular disease, which may help to improve the interpretability and description in clinic and clinical trials. Further validation in these settings is warranted and ongoing. Efforts are continuing to address unresolved questions.

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)0.178
metaresearch head score (Gemma)0.367
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.178
Threshold uncertainty score0.944

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1780.367
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.004
Bibliometrics0.0080.005
Science and technology studies0.0040.004
Scholarly communication0.0050.006
Open science0.0080.006
Research integrity0.0110.012
Insufficient payload (model declined to judge)0.0040.004

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 head0.059
GPT teacher head0.358
Teacher spread0.299 · 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 designNot applicable
Domainnot available
GenreMethods

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
Published2022
Admission routes1
Has abstractyes

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