Transient vision and intraocular pressure changes following anti-vascular endothelial growth factor injection
Bibliographic record
Abstract
OBJECTIVE: To assess transient intraocular pressure (IOP) and visual acuity (VA) changes following anti-vascular endothelial growth factor (anti-VEGF) injections and explore factors influencing recovery. DESIGN: A prospective observational study. PARTICIPANTS: Eighty-six patients (100 eyes) receiving anti-VEGF injections with either aflibercept, bevacizumab, or ranibizumab at a retina clinic were included. METHODS: Age, biological sex, diagnosis (neovascular age-related macular degeneration, retinal vein occlusion, diabetic macular edema), antiseptic used, anti-VEGF agent, glaucoma status, and IOP-lowering pretreatment were collected. IOP and VA using Snellen charts were measured at baseline, 1, 10, 20, and 30 minutes after injection. Spearman's correlation coefficients were used to assess the relationships between IOP and VA. Ordinal logistic regression was used to evaluate predictors of delayed VA recovery. RESULTS: At 1 minute after intravitreal injection (IVI), VA worsened significantly from a baseline of 0.29 ± 0.21 to 0.76 ± 0.65 logMAR (p < 0.001), while IOP rose from 14.34 ± 4.39 mm Hg to 54.53 ± 20.21 mm Hg (p < 0.001). VA progressively improved over time, with 43% of eyes returning to baseline at 1 minute, 67% at 10 minutes, 83% at 20 minutes, and 89% at 30 minutes. The Spearman correlation coefficient for VA and IOP was statistically significant at 1-minute follow-up after IVI at 0.244 (p = 0.014) but not at later follow-ups. Eyes returning to baseline VA at 1 minute exhibited lower IOP than those not at baseline (p = 0.0004). Logistic regression revealed no significant predictors of delayed VA recovery. CONCLUSIONS: Worsened VA and elevated IOP are common in the immediate postinjection period with later improvement. Patients' awareness of VA fluctuations within the first 30 minutes postinjection may aid in the early detection and management of complications associated with IOP elevation.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".