Population-based analysis of intraocular lens exchange and repositioning
Bibliographic record
Abstract
PURPOSE: To determine the incidence and trends in intraocular lens (IOL) repositioning, exchange, and explantation. SETTING: Department of Ophthalmology and Vision Sciences, University of Toronto, Toronto, Ontario, Canada. DESIGN: Population-based retrospective data analysis. METHODS: Service claims from 2000 to 2013 were analyzed for the total yearly number of IOL repositionings, exchanges, and explantations in Ontario, Canada, including the number of surgeons performing them by subspecialty. The 5-year incidence proportion of secondary IOL surgery for patients who had cataract surgery in 2000 and 2009 was calculated and then stratified by sex, age, and year of second surgery. RESULTS: Of the 1252 secondary procedures performed in 2013 (75.6% increase from 2000), 43.2% were repositionings without suturing, 31.6% were exchanges without suturing, 10.5% were sutured repositionings, 7.0% were sutured exchanges, and 7.7% were explantations. The incidence proportion of risk for secondary IOL surgery was 0.93% from 2000 to 2004, which decreased to 0.78% from 2009 to 2013 (16.4% decrease; odds ratio, 0.83; 95% confidence interval [CI], 0.72-0.94; P < .001). Patients who had these procedures were 1.56 times more likely to be men (95% CI, 1.39-1.76; P < .001) and 1.52 times more likely to be younger than 65 years (95% CI, 1.33-1.73; P < .001). From 2000 to 2013, sutured repositionings and explantations increased by 568% and 531%, respectively, whereas exchanges without suturing decreased by 22.6%. In 2013, 11.6% of surgeons performed 52.0% of all secondary IOL surgeries. CONCLUSIONS: Although the absolute number of secondary IOL procedures increased from 2000 to 2013, the 5-year risk for surgery decreased. A large proportion of the surgeries was performed by a small number of surgeons, which suggests subspecialization.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".