Diagnostic performance of optical coherence tomography angiography in glaucoma: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Compared with current imaging methods, the diagnostic performance and the advantages and limitations of optical coherence tomography angiography (OCTA) remain unclear. We performed a systematic review and meta-analysis of studies investigating vessel density (VD) in patients with glaucoma using OCTA. METHODS: We conducted a literature search on PubMed, Scopus, Web of Science, ISI Conference Proceedings and Google Scholar, along with a manual search, from January 2006 to March 2018. We included prospective studies that used OCTA to compare the VD in glaucomatous eyes with healthy control eyes. RESULTS: Of 3045 screened articles, 24 were included in a broad characterisation and 18 in the meta-analysis. We observed a statistically significant reduction in the mean peripapillary VD (MPVD) in glaucoma (MPVD: 57.53%, 95% CI 52.60 to 62.46, p< 0.001) compared with controls (MPVD: 65.47%, 95% CI 59.82 to 71.11; standardised mean difference [SMD], -1.41, 95% CI -1.62 to -1.20, p< 0.001) for 888 glaucomatous and 475 healthy eyes, and also in the mean-whole optic nerve image VD (SMD, -9.63, 95% CI -10.22 to -9.03, p<0.001), mean inside-disc VD (SMD, - 9.51, 95% CI -12.66 to -6.36, p<0.05) and mean parafoveal VD (SMD, -3.92, 95% CI -4.73 to -3.12, p<0.001). Subgroup analyses revealed a significant difference in the MPVD across glaucoma subtypes and OCTA devices. CONCLUSION: This suggests the diagnostic utility of OCTA in detecting glaucomatous eyes; however, further longitudinal prospective studies are welcomed to characterise vascular changes in glaucoma.
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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 | 0.018 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.034 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".