Laser Trabeculoplasty Trends With the Introduction of New Medical Treatments and Selective Laser Trabeculoplasty
Bibliographic record
Abstract
PURPOSE: To correlate trends of laser trabeculoplasties (LTPs) with the introduction of medical therapies for glaucoma and to assess whether these trends changed after the introduction of selective laser trabeculoplasty (SLT) in 2001. METHODS: A retrospective analysis of LTP numbers, filtration surgeries, glaucoma medications dispensed, and population distribution by age in Ontario, Canada, between April 1992 and March 2005. RESULTS: The number of LTP per 1,000 persons estimated to have primary open angle glaucoma (POAG) increased from 138.05 in 1992 to a maximum of 149.23 in 1996 (8.1% increase, 1.96% annual increase) and then steadily decreased to 70.65 in 2001 (47.3% decrease, 14% annual decrease). From 2001 to 2004, the LTP rate increased to 162.54 (230% increase, 32% annual increase). The number of filtration surgeries per 1,000 persons estimated to have POAG steadily decreased from 1996 to 2004 by 21.42% (2.4% annual decrease). The number of glaucoma medications dispensed in Ontario increased from 1992 to 2004 by 91.5% (10.5% annual increase). There were no significant correlations between the LTP rates and the new glaucoma medications rates (r=-0.35 to 0.09; P=0.34 to 0.82) or filtration surgeries rates (r=0.007; P=0.98). CONCLUSIONS: There was a substantial reduction in the number of LTP between 1997 and 2001 coinciding, but not correlated with the introduction of medications for the treatment of glaucoma. Between 2002 and 2004 the LTP rates increased, coinciding with the introduction of SLT.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".