Central Corneal Thickness in Children and Adolescents with Pediatric Glaucoma and Eye Disorders at Risk of Developing Glaucoma
Bibliographic record
Abstract
BACKGROUND: To investigate central corneal thickness (CCT) in children with glaucoma and at risk for glaucoma. METHODS: The study included 139 children with glaucoma: 66 at risk for glaucoma (ie, aphakia, aniridia, or uveitis) and 66 normal children. CCT was measured by ultrasound pachymetry and intraocular pressure (IOP) by applanation. Analysis of variance was used to compare CCT between groups. Correlation analysis assessed associations between CCT and ocular factors including spherical equivalent, cup-to-disc ratio, glaucoma medications, and number of intraocular surgeries. RESULTS: CCT was significantly higher for 141 eyes with glaucoma (mean: 0.598 mm, P < .001) and 76 eyes at risk for glaucoma (mean: 0.604 mm, P = .001) than for 66 normal eyes (mean: 0.558 mm). No significant difference was observed between at-risk (P = .989) and glaucoma eyes. Eyes with aphakia (0.653 mm) and aniridia (0.639 mm) had the thickest CCT values. Thinnest CCT was found in anterior segment dysgenesis and uveitis (mean: 0.541 mm). A significant positive correlation between CCT and spherical equivalent was found for glaucoma (r = 0.413; P < .001) and at-risk (r = 0.412; P < .0003) eyes, and between CCT and intraocular surgery for at-risk eyes (P = .0066). A significant negative correlation was found between CCT and cup-to-disc ratio for glaucoma eyes (r = -0.223; P = .01). CONCLUSION: This is the largest series of CCT in pediatric glaucoma and related disorders. The data suggest caution in application of standard formulas for IOP-to-CCT correction when evaluating children with glaucoma because their mean CCT values extend far beyond values reported for normal eyes.
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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 | 0.003 |
| 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 | 0.001 |
| 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".