Risk of ptosis following eyelid speculum assisted intravitreal anti-VEGF injections
Bibliographic record
Abstract
PURPOSE: Intravitreal injections are essential for treating retinal diseases. This study aims to assess the impact of repeated intravitreal anti-VEGF injections using an eyelid speculum on the risk of ptosis development. METHODS: This single-center, retrospective chart review included 114 patients (228 eyes) who received at least three unilateral intravitreal anti-VEGF injections. Patient demographics, clinical characteristics, and MRD1 and MRD2 of the injected and the fellow eyes were analyzed. A multivariate linear regression model was constructed to identify predictors of MRD1 in the injected eye. RESULTS: The study cohort had a mean age of 75.18 ± 0.98 years, with 57% female patients. On average, patients received 16.92 ± 1.18 injections. At the final follow-up, no significant difference was observed in mean MRD1 between injected and fellow eyes (2.85 ± 0.11 mm vs. 2.90 ± 0.11 mm, p = 0.445). Multivariate regression analysis identified MRD1 of the fellow eye as the only significant predictor of MRD1 in the injected eye (β = 0.769, p < 0.001). CONCLUSION: The repeated use of an eyelid speculum during intravitreal anti-VEGF injections does not significantly contribute to ptosis development. MRD1 tends to be similar between the injected and non-injected eye, suggesting that intrinsic factors may play a more crucial role in determining eyelid position than the mechanical effects of the procedure.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".