The Effect of Contact Lens Induced Corneal Edema on Goldmann Applanation Tonometry Measurements
Bibliographic record
Abstract
AIM: To determine the effect of small increases in corneal hydration on the accuracy of Goldmann applanation tonometry estimates of intraocular pressure (IOP). MATERIALS AND METHODS: Twenty-five young healthy subjects presented on 3 separate days approximately 1 week apart. On 2 visits, subjects were required to wear a hydrogel contact lens with either a center thickness of 0.3 and 0.7 mm (HEMA 38% water content, parallel surface curve) in 1 eye only under closed-eye conditions for 2 hours to induce corneal swelling. The third visit acted as a control. IOP, corneal thickness, and corneal curvature were measured in both eyes before and after contact lens wear on all visits. RESULTS: There was a statistically significant increase in corneal thickness of 40.2+/-14.4 microm (P<0.001) and 41.9+/-16.4 microm (P<0.001) after wearing the 0.3 and 0.7 mm thick contact lenses, respectively (2-tailed paired t test). There was an increase in IOP of 2.8+/-2.2 mm Hg (P<0.001) after wearing the 0.3 mm thick contact lens, and a statistically insignificant difference of 1.3+/-3.0 mm Hg (P=0.058) after wearing the 0.7 mm thick contact lens (2-tailed paired t test). There was a statistically significant Pearson correlation between the change in corneal thickness and the change in IOP after lens wear (r=0.500, P<0.001, 0.3 mm lens and r=0.399, P<0.001, 0.7 mm lens). The corneal hydration-induced measurement error was 0.46 mm Hg per 10 microm change in corneal thickness (0.3 mm lens) and 0.35 mm Hg per 10 microm change in corneal thickness (0.7 mm lens). CONCLUSION: A small increase in corneal hydration and thickness may cause a clinically significant overestimation of IOP when measured using Goldmann applanation tonometry.
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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 | 0.009 |
| 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".