Relationship Between Central Corneal Thickness and Changes of Optic Nerve Head Topography and Blood Flow After Intraocular Pressure Reduction in Open-angle Glaucoma and Ocular Hypertension
Bibliographic record
Abstract
OBJECTIVES: To investigate changes in optic nerve head topography and blood flow after therapeutic intraocular pressure reduction and to correlate them with central corneal thickness. METHODS: Sixteen patients with open-angle glaucoma and 16 patients with ocular hypertension underwent Heidelberg retina tomography and scanning laser Doppler flowmetry in 1 eye before and at least 2 months after a mean 35% sustained therapeutic reduction in intraocular pressure. Patients were assigned to a thin or thick group based on their median central corneal thickness. RESULTS: Compared with 16 patients with thick corneas (mean +/- SD central corneal thickness, 587 +/- 31 microm), the 16 patients with thin corneas (518 +/- 32 microm) had greater reductions in mean (36 +/- 32 vs 4 +/- 36 microm, P = .003) and in maximum cup depth (73 +/- 107 vs 4 +/- 89 microm, P = .02). These changes were not statistically significantly different between the patients with open-angle glaucoma and those with ocular hypertension. Smaller mean +/- SD improvements in neuroretinal rim blood flow were seen in patients with thinner corneas compared with those with thicker corneas (35 +/- 80 vs 110 +/- 111 arbitrary units, P = .04). CONCLUSION: Patients with open-angle glaucoma and ocular hypertension with thinner corneas show significantly greater shallowing of the cup, a surrogate marker for lamina cribrosa displacement (compliance), and smaller improvements of neuroretinal rim blood flow after intraocular pressure reduction.
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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 | 0.003 |
| 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".