Laser peripheral iridotomy across the spectrum of primary angle closure
Bibliographic record
Abstract
BACKGROUND: To evaluate trends in cataract surgeries in Ontario between 1992 and 2004. METHODS: A retrospective analysis of the number of cataract surgeries performed in Ontario from April 1992 to March 2005. The estimated prevalence of cataract and cataract surgeries per 1000 persons at risk was calculated. RESULTS: The number of cataract surgeries in Ontario increased from 44,943 in 1992 to 109,506 in 2004 (143.6%, 12.08% annual increase). The number of cataract surgeries per 1000 patients at risk of cataract increased from 64.6 in 1992 to 115.65 in 2004 (79%, 4.97% increase per year). This rate was strongly positively correlated with time and with the increase in the Ontario population (r = 0.920 and r = 0.922, respectively; p < 0.001). The number of ophthalmologists increased by 5.3% from 1992 to 1997 and then decreased by 2.9% by 2004. This change was not correlated with the cataract surgery rates (r = 0.475; p = 0.10). However, the number of ophthalmologists per million population decreased by 13.4% between 1992 and 2004. This number had a statistically negative correlation with cataract surgery rates (r = -0.757; p < 0.01). INTERPRETATION: There has been a significant increase in the number of cataract surgeries in Ontario despite a decrease in the number of ophthalmologists per million population.
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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 | 0.001 |
| 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) | 0.003 | 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".