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.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.014 | 0.007 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".