Intraocular Pressure While Using Gonioscopy, SLT, and Laser Iridotomy Lenses: An Ex Vivo Study
Bibliographic record
Abstract
Purpose: The purpose of this study was to measure intraocular pressure (IOP) elevation while applying standard gonioscopy, selective laser trabeculoplasty (SLT), and laser iridotomy procedural lenses. Methods: Twelve cadaver eyes were mounted to a custom apparatus and cannulated with a pressure transducer which measured IOP. The apparatus was mounted to a load cell which measured the force on the eye. Six ophthalmologists performed simulated gonioscopy (Sussman 4 mirror lens), SLT (Latina lens), and laser iridotomy (Abraham lens) while a computer recorded IOP (mm Hg) and force (grams). The main outcome measures were IOP and force applied to the eye globe during ophthalmic diagnostics and procedures. Results: The average IOP's during gonioscopy, SLT, and laser iridotomy were 43.2 ± 16.9 mm Hg, 39.8 ± 9.9 mm Hg, and 42.7 ± 12.6 mm Hg, respectively. The mean force on the eye for the Sussman, Latina, and Abraham lens was 40.3 ± 26.4 grams, 66.7 ± 29.8 grams, and 65.5 ± 35.9 grams, respectively. The average force applied to the eye by the Sussman lens was significantly lower than both the Latina lens (P = 0.0008) and the Abraham lens (P = 0.001). During gonioscopy indentation, IOP elevated on average to 80.5 ± 22.6 mm Hg. During simulated laser iridotomy tamponade, IOP elevated on average to 82.3 ± 27.2 mm Hg. Conclusions: In cadaver eyes, the use of standard ophthalmic procedural lenses elevated IOP by approximately 20 mm Hg above baseline.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".