Effect of ginkgo biloba extract on macula and peripapillary perfusion examined using optical coherence tomography angiography
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effect of ginkgo biloba extract (GBE) on optical coherence tomography angiography (OCT-A) macula and peripapillary perfusion parameters among patients with treated early-to-moderate primary open-angle glaucoma. DESIGN: Clinical trial. PARTICIPANTS: Seventeen patients with early-to-moderate (≥10 dB MD) primary open-angle glaucoma were matched to 17 control patients based on age, sex, and glaucoma status. A total sample size of 34 was determined for effect size 0.5, alpha 0.05, power 0.81, and critical t = 2.03. Normality was confirmed using the Kolmogorov-Smirnov and Shapiro-Wilk tests. METHODS: The intervention was 120 mg oral GBE twice daily for 4 months. OCT-A scans (15° × 15°) of the macula and peripapillary retina were acquired, two-dimensional projection slab images of the superficial vascular complex were exported, and image analysis was performed. Student's t test was used to compare perfusion density between groups, and between baseline and follow-up for each group. The main outcomes were perfusion density of the superficial vascular complex of the macula and the peripapillary region. RESULTS: Comparison between baseline and 4 months' supplementation with GBE revealed no significant change in perfusion density in the macular area, 0.32 (0.04) versus 0.30 (0.04); p = 0.17, and was significantly lower in the peripapillary area, 0.44 (0.05) versus 0.42 (0.04); p = 0.02. No differences were observed in the control group. CONCLUSION: Four-month supplementation with GBE did not result in clinically significant improvement in macula or peripapillary perfusion density in patients with treated early-to-moderate primary open-angle glaucoma. Larger studies are needed to confirm an absence of neuroprotective effects of GBE.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".