Risk Factors for Visual Field Progression in the Groningen Longitudinal Glaucoma Study
Bibliographic record
Abstract
PURPOSE: To identify risk factors for visual field progression in glaucoma and to compare different statistical approaches with this risk factor analysis. PATIENTS AND METHODS: We included 221 eyes of 221 patients. Progression was analyzed using Nonparametric Progression Analysis applied to Humphrey Field Analyzer data. Risk factors were analyzed using the statistical approaches from the Advanced Glaucoma Intervention Study, the Early Manifest Glaucoma Trial, and the Canadian Glaucoma Study. Four intraocular pressure (IOP) variables (baseline IOP, mean IOP during follow-up, IOP fluctuation, and pretreatment IOP) and 8 other risk factors were investigated. RESULTS: On average, 7.1 reliable fields were available after a mean follow-up of 5.3 years; 89 eyes progressed. With the Advanced Glaucoma Intervention Study approach, age [odds ratio (OR) 1.03/y; 95% confidence interval (CI), 1.00-1.06; P = 0.044] predicted progression. With an additional stepwise selection procedure, mean IOP during follow-up (1.16 per mm Hg; 1.05-1.29; P=0.003),baseline HFA mean deviation (MD; 2.72 for worse versus better than --6 dB; 1.50-4.95; P=0.001) and age (1.03; 1.01-1.06;P=0.010) predicted progression [corrected]. With the Early Manifest Glaucoma Trial approach, baseline IOP [hazard ratio (HR) 1.07; 95% CI, 1.02-1.11; P = 0.010], baseline Frequency Doubling Perimeter MD (HR = 1.75; 95% CI, 1.14-2.70; P = 0.013), and age (HR = 1.03; 95% CI, 1.01-1.05; P = 0.006) predicted progression. Finally, with the Canadian Glaucoma Study approach, baseline IOP (HR = 1.07; 95% CI, 1.02-1.11; P = 0.010), baseline Frequency Doubling Perimeter MD (HR = 1.75; 95% CI, 1.14-2.70; P = 0.013), and age (HR = 1.03; 95% CI, 1.01-1.05; P = 0.006) predicted progression. CONCLUSIONS: IOP, disease stage, and age seemed to be robust independent risk factors for visual field progression in glaucoma. The IOP variable that was significant depended on the statistical approach applied.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".