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Record W4411593136 · doi:10.1016/j.ophtha.2025.06.018

Baseline OCT Biomarkers Predicting Visual Outcomes in Neovascular Age-Related Macular Degeneration

2025· review· en· W4411593136 on OpenAlexaff
Keean Nanji, Justin Grad, Amin Hatamnejad, Tyler McKechnie, Mark H. Phillips, Chui Ming Gemmy Cheung, Praveen J. Patel, Rosa Dolz Marco, Enrico Borrelli, David Steel, SriniVas R. Sadda, Tien Yin Wong, Sobha Sivaprasad, Robyn H. Guymer, Charles C. Wykoff, Varun Chaudhary

Bibliographic record

VenueOphthalmology · 2025
Typereview
Languageen
FieldMedicine
TopicRetinal Diseases and Treatments
Canadian institutionsImpactMcMaster University
Fundersnot available
KeywordsMedicineMacular degenerationMeta-analysisOphthalmologyBaseline (sea)Visual acuityInternal medicine

Abstract

fetched live from OpenAlex

TOPIC: To determine the effect estimates and certainty of evidence for baseline OCT biomarkers predicting visual acuity (VA) and changes in VA from baseline at 6, 12, and 24 months after anti-vascular endothelial growth factor therapy for neovascular age-related macular degeneration. CLINICAL RELEVANCE: Understanding the prognostic utility of biomarkers can improve treatment decisions. METHODS: Results were reported in ETDRS letters. Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) guidelines for prognostic studies informed certainty of evidence. Results were interpreted using a 5-letter minimally important difference. RESULTS: Twenty-nine reports (8863 eyes) evaluating 80 biomarkers were included. Two biomarkers predicted better VA at 12 months with a low-certainty: the presence of an intact external limiting membrane (+14.0; 95% confidence interval [CI], +3.1 to +24.8) and the presence of an intact ellipsoid zone (+6.8; 95% CI, +2.8 to +10.8). Three biomarkers predicted worse VA at 12 months with a low certainty; the presence of intraretinal fluid (IRF; -5.6; 95% CI, -9.7 to -1.5), the presence of IRF in the foveal center point (-7.4; 95% CI, -10.1 to -4.7), and the presence of subretinal hyperreflective material (-8.7; 95% CI, -19.0 to 1.6). No other biomarker predicted an effect size that crossed the minimally important difference. However, noteworthy results occurred when interpreting biomarkers with statistically significant findings relative to a threshold of 0 letters and moderate certainty: the presence of a pigment epithelial detachment, geographic atrophy (GA), and both IRF and subretinal fluid (SRF) predicted reduced vision at 12 months. The presence of SRF predicted a positive change in VA at 12 months. The absence of a posterior vitreous detachment predicted a negative change in VA at 12 months. Finally, the presence of IRF in the central 1 mm, retinal pigment epithelial elevation, and GA predicted negative changes in VA at 24 months. DISCUSSION: With low-certainty evidence, the baseline presence of an intact external limiting membrane and ellipsoid zone predicted better VA at 12 months, and the presence of IRF, IRF in the foveal center point, and subretinal hyperreflective material predicted worse VA at 12 months. Improved standardization in biomarker classification and control of confounding variables is needed. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.033
GPT teacher head0.389
Teacher spread0.356 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations16
Published2025
Admission routes1
Has abstractno

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