The Role of OCTA in Evaluating Diabetic Retinopathy Progression: A Meta-Analysis.
Bibliographic record
Abstract
Background: Diabetic retinopathy (DR) substantially threatens ocular health, necessitating the accurate and prompt assessment of its onset and progression. Optical coherence tomography angiography (OCTA) is a valuable tool for evaluating periocular microvascular indicators, offering insights crucial for diagnosing and treating DR. Objective: This meta-analysis aims to evaluate the progression of diabetic retinopathy (DR) by examining periocular microvascular indicators using optical coherence tomography angiography (OCTA). The objective is to provide substantive evidence for the future diagnosis and treatment of DR. Methods: We analyzed the relevant research retrieved from PubMed and Web of Science until January 2023. The inclusion and exclusion criteria were carefully applied to select eligible studies. Quality assessment was performed using the Newcastle-Ottawa Scale, with studies scoring 4 or less excluded. Meta-analysis was conducted using Revman 5.3 software and focused on key indicators, including peripapillary vascular length density (pVLD) and peripapillary vascular density (pVD). Heterogeneity was assessed using I2 and P values, with effect sizes determined via fixed-effect or random-effects models based on heterogeneity levels. Results: Six studies involving 839 DR-afflicted eyes and 3209 non-DR eyes were included after screening. All selected articles exhibited high reference value, with quality scores ranging from 5 to 8 points. The meta-analysis demonstrated that DR patients displayed significantly lower pVD and pVLD in the superficial (SCP) and deep capillary plexus (DCP) compared to non-DR patients (P < .05). These findings remained consistent across different effect models, reaffirming their validity. Conclusions: Patients with DR exhibit reduced levels of pVD and pVLD in the SCP and DCP compared to non-DR individuals. OCTA examination of periocular microvascular indicators emerges as an effective tool for assessing the onset and progression of DR.
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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 | 0.024 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.067 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".