Association between corneal thickness, mean intraocular pressure, disease stability and severity, and cost of treatment in glaucoma: a Canadian analysis
Bibliographic record
Abstract
PURPOSE: We determined the association between the mean corneal thickness (CT) and visual field mean defect (VF) severity as well as with mean intraocular pressure (IOP), disease stability, and cost of glaucoma therapy in a Canadian setting. METHODS: Data were collected from charts of patients diagnosed with primary open-angle glaucoma (POAG). CT measures, VF scores, IOP measurements, physicians' impressions, and resources used (physician visits, diagnostic tests, procedures, and medications) were recorded over a minimum of 2.5 years. CT was compared across the three VF severity levels [mild (0 to < 5 dB), moderate (5 to < 12 dB), and severe (>/= 12 dB)] using a Kruskall-Wallis test. Initial VF was regressed on Age, CT, IOP, and Optic Disc Ratio. Stability and Cost were regressed on IOP. RESULTS: Of the 411 charts, 132 included CT measures. Patients included 50 with mild, 43 with moderate, and 39 with severe disease. The mean CTs of the overall, mild, moderate, and severe groups were 545.9 mum, 554.7 mum, 549.8 mum, and 523.3 mum, respectively. There were statistically significant differences (p < 0.05) between the CT pp of the mild and severe groups as well as between the moderate and severe groups. Regression analyses suggested that CT may be a predictor of disease severity, but not of cost. It was also found that IOP may be a predictor of disease progression. CONCLUSIONS: Patients with severe VFs tend to be those who have thinner corneas. Further research is warranted, as a result of the limited sample size, to clarify the definitive association among corneal thickness, disease progression, and the cost of therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".