Correlation of ocular rigidity with intraocular pressure spike after intravitreal injection of bevacizumab in exudative retinal disease
Bibliographic record
Abstract
BACKGROUND/AIMS: To evaluate the non-invasive measurement of ocular rigidity (OR), an important biomechanical property of the eye, as a predictor of intraocular pressure (IOP) elevation after anti-vascular endothelial growth factor (anti-VEGF) intravitreal injection (IVI). METHODS: Subjects requiring IVI of anti-VEGF for a pre-existing retinal condition were enrolled in this prospective cross-sectional study. OR was assessed in 18 eyes of 18 participants by measurement of pulsatile choroidal volume change using video-rate optical coherence tomography, and pulsatile IOP change using dynamic contour tonometry. IOP was measured using Tono-Pen XL before and immediately following the injection and was correlated with OR. RESULTS: The average increase in IOP following IVI was 19±9 mm Hg, with a range of 7-33 mm Hg. The Spearman correlation coefficient between OR and IOP elevation following IVI was 0.796 (p<0.001), showing higher IOP elevation in more rigid eyes. A regression line was also calculated to predict the IOP spike based on the OR coefficient, such that IOP spike=664.17 mm Hg·µL×OR + 4.59 mm Hg. CONCLUSION: This study shows a strong positive correlation between OR and acute IOP elevation following IVI. These findings indicate that the non-invasive measurement of OR could be an effective tool in identifying patients at risk of IOP spikes following IVI.
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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 | 0.001 |
| 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) | 0.002 | 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".