LARGE RETINAL CAPILLARY ANEURYSM
Bibliographic record
Abstract
PURPOSE: To develop consensus nomenclature amongst international retinal specialists for the distinctive optical coherence tomography (OCT) finding of a lesion originating from the retinal capillary bed, measuring ≥100 µm in size, and characterized by a hyperreflective wall with a hyporeflective lumen. METHODS: A comprehensive literature search was performed from inception to January 2024 on three databases to elicit publications reporting on relevant vascular abnormalities and corresponding nomenclature. A panel of retinal specialists with expertise in this topic reviewed the list of candidate terms and proposed other names for the lesion of interest. A refined list was then incorporated into a Delphi survey, which was distributed to the general membership of the International Retinal Imaging Society (IntRIS). Consensus was defined as at least 70% agreement amongst participants. RESULTS: An expert panel (n=11) reviewed candidate names for the lesion, with poor agreement noted amongst panel members regarding the relevant nomenclature. In the first Delphi survey, (n=70 IntRIS members), the need for a unified nomenclature was highlighted and two leading candidate names were established: large retinal capillary aneurysm (LRCA, n=38, 54.3%) and retinal capillary macroaneurysm (n=14, 20.0%). A second follow-up survey (n=54 IntRIS members) established LRCA (n=44, 81.5%) as the consensus term to identify the OCT vascular abnormality. CONCLUSION: This Delphi project reached consensus on a unifying term, large retinal capillary aneurysm, for a specific and signature OCT lesion. Identification of this characteristic OCT finding and adoption of this term may facilitate diagnosis, guide therapeutic decisions, and improve clinical and scientific communication.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".