Effect of Medical Therapy on Glaucoma Filtration Surgery Rates in Ontario
Bibliographic record
Abstract
OBJECTIVE: To analyze trends of glaucoma filtration surgery in Ontario. METHODS: From April 1, 1992, through March 31, 2004, correlations were examined between the annual rates of trabeculectomies in Ontario, the use of glaucoma medications, and the numbers of practicing ophthalmologists and optometrists. RESULTS: The number of trabeculectomies per 1000 persons at risk for primary open-angle glaucoma increased from 33.5 in 1992 to 46.2 in 1996 (37.7% increase; 6.6% increase per year) and then steadily decreased to 38.2 in 2004 (17.0% decrease; 2.7% decrease per year). The number of glaucoma medications dispensed in Ontario increased from 766 000 in 1992 to 1 466 543 in 2004 (91.5% increase; 10.5% annual increase). The increase in dispensed prostaglandin analogues strongly correlated (P<.001; 95% confidence interval, -0.87 to -0.41) with the decreasing number of trabeculectomies. The decreasing number of ophthalmologists positively correlated (r = 0.87) with the filtration surgery rate after 1997. CONCLUSIONS: The number of trabeculectomies has decreased substantially in Ontario coinciding with the introduction of medications for the treatment of glaucoma in December 1996. This decrease in trabeculectomies highly correlated with the introduction of prostaglandin analogues (P<.001) and the decreasing number of ophthalmologists from 1997 through 2004.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".