Cost of Glaucoma in Canada: Analyses Based on Visual Field and Physician's Assessment
Bibliographic record
Abstract
PURPOSE: A longitudinal, retrospective study investigated the cost of primary open angle glaucoma (POAG). METHODS: Patient files from two tertiary care glaucoma practices were reviewed. Patients diagnosed with POAG and >/=2.5 years of follow-up data were included. Data collected included visual field mean deviation, physician's assessment, and resource utilization (physician visits, procedures, and medications). Costs, reported in 2001 Canadian dollars, were compared between groups, based on initial visual field mean deviation, including mild (<5 dB), moderate (5 to <12 dB), and severe (>/=12 dB), and based on physician's assessment, including controlled, uncontrolled, or patients initially uncontrolled for 12 months who become controlled. RESULTS: Of 411 patient charts extracted, 265 were included; 35 were excluded for ocular comorbidities and 111 patients with insufficient follow-up. Mean (standard deviation) yearly costs overall (N = 265) and for mild (n = 90), moderate (n = 91), and severe (n = 84) groups were $508 ($278), $408 ($266), $512 ($288), and $609 ($243), respectively. Differences between mean yearly costs were statistically significant for all three groups (P < 0.05). Costs for controlled (n = 110), uncontrolled (n = 76), and uncontrolled then controlled group (n = 79) were $423 ($243), $594 ($314), and $542 ($256), respectively. The controlled group cost was significantly lower than both of the other groups (P < 0.05). DISCUSSION AND CONCLUSIONS: The cost of treating POAG increases with visual field mean deviation severity and uncontrolled disease. Many patients diagnosed with glaucoma had already progressed to later stages in the disease process. Early disease detection may provide a substantial cost savings to the health care system.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".