Use of retinal ischemic perivascular lesions (RIPLS) as a biomarker for cardiovascular disease – a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: This study aims to evaluate the association between retinal ischemic perivascular lesions (RIPLs) detected by optical coherence tomography (OCT) and the risk of all-cause cardiovascular morbidity. METHODS: A systematic review and meta-analysis were performed. PubMed MEDLINE, Scopus, and Cochrane CENTRAL were searched for observational studies from January 2015 to March 2025 assessing RIPLs and cardiovascular disease (CVD). Odds ratios (ORs) with 95% confidence intervals (CIs) were pooled using a random-effects model. RESULTS: Of 61 studies screened, six met inclusion criteria and four were included in the meta-analysis (total n = 710). The pooled OR for CVD morbidity associated with RIPLs was 2.8 (95% CI: 1.98–3.95), indicating over a twofold increased risk. Heterogeneity was minimal (I² = 0%). CONCLUSIONS: RIPLs detected by OCT are significantly associated with increased cardiovascular risk. Due to OCT’s non-invasive and accessible nature, RIPLs may be a useful screening biomarker for early detection of CVD.
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.033 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".