Changes in Scleral Thickness Following Repeated Anti-vascular Endothelial Growth Factor Injections
Bibliographic record
Abstract
Purpose: This cross-sectional study aimed to compare changes in scleral thickness between eyes injected with repeated anti-vascular endothelial growth factor (anti-VEGF) drugs and fellow injection naive eyes using optical coherence tomography (OCT). Methods: A total of 79 patients treated with three intravitreal anti-VEGF injections in one eye versus no injections in the fellow eye were included. Anterior segment- OCT measured scleral thickness in the inferotemporal quadrant 4 mm away from the limbus. Results: Injected eyes had a mean scleral thickness of 588 ± 95 μm versus 618 ± 85 μm in fellow naïve eyes (P < 0.001). Comparing injected eyes to fellow naïve eyes stratified by injection number showed a mean scleral thickness of 585 ± 93 μm versus 615 ± 83 μm in eyes with 3–10 injections (n = 32, P = 0.042); 606 ± 90 μm versus 636 ± 79 μm in eyes with 11–20 injections (n = 24, P = 0.017); and 573 ± 104 μm versus 604 ± 93 μm in eyes with >20 injections (n = 23, P = 0.041). There was no significant correlation between injection number and scleral thickness change (r = –0.07, P = 0.26). When stratified by indication, subjects with retinal vein occlusions showed a statistically significant difference in scleral thickness between injected and fellow naïve eyes (535 ± 94 μm and 598 ± 101 μm, respectively, P = 0.001). Conclusion: Compared to injection naive eyes, multiple intravitreal injections at the repeated scleral quadrant results in scleral thinning. Consideration of multiple injection sites should be considered to avoid these changes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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 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".