Optical coherence tomography angiography (OCT angiography) in anterior ischemic optic neuropathy (AION): A systematic review and meta-analysis
Why this work is in the frame
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Bibliographic record
Abstract
Purpose To summarize the evidence available on optical coherence tomography angiography (OCTA) in patients with anterior ischemic optic neuropathy (AION). Methods Systematic searches were conducted on PubMed, Embase, Web of Science, Scopus, Cochrane, and Google Scholar Databases. The quality assessment of the included studies was performed using Newcastle -Ottawa Scale. The data were extracted to an Excel sheet. Vessel density (VD) data were pooled by random effects model, presented as pooled percentage change (PPC), and weighted mean differences (WMD). Additional subgroup analysis was also conducted. Results In initial searches in online databases, we found 3535 citations, and after screening and checking the titles and abstracts, 26 articles were ultimately eligible for our meta-analysis. The overall PPC of Intra-optic-disc (IOD) VD (−10.73%; p = 0.017, I 2 = 0.0%; p = 0.898) was lower than that of radial peripapillary (RP) VD (−17.57%; p < 0.001, I 2 = 44.3%; p = 0.002). The overall PPC of peripapillary choroid VD (−6.99%; p < 0.001, I 2 = 0.0%; p = 0.766) was significant, but noticeably lower than the pooled percentage change of RPVD and IOD VD. The WMD of RPVD was significant when non-affected fellow eyes were compared to the healthy subjects’ eyes (−36.26; p < 0.001, I 2 = 0.0%; p = 0.706). Conclusions The central retinal artery and its branches might be the main vessels which are affected in AION. The superficial retina was more affected than choroid layer in AION. Also, radial peripapillary retinal nerve fibre layer was more affected than the IOD area. OCTA might be a suitable tool for prediction of AION in susceptible eyes.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it