Rate of retinal tear and detachment after neodymium:YAG capsulotomy
Bibliographic record
Abstract
PURPOSE: To determine the rate of retinal tear and retinal detachment (RD) after neodymium:YAG (Nd:YAG) laser capsulotomy for posterior capsule opacification (PCO) after cataract surgery. SETTING: Province-wide outpatient and hospital settings, Alberta, Canada. DESIGN: Database study. METHODS: Eleven years of billing records data were collected to assess the rate of retinal tear and/or RD after Nd:YAG laser capsulotomy. A period of 90 days from Nd:YAG was considered the at-risk period, although statistics for 10 years of data were included in the study. Risk was calculated as a rate (%) of retinal tear or RD after Nd:YAG laser capsulotomy. RESULTS: The study comprised 92 654 discrete billing records yielding 73 586 ocular procedures for the analysis of the rate of retinal tear and/or RD after Nd:YAG laser capsulotomy. There were 67 287 Nd:YAG capsulotomies for PCO performed during the study. The 90-day risk for retinal tear after Nd:YAG was 0.21%; 720 retinal tears occurred in the study population at some point after the procedure. The rate of RD was 0.60%, with 2219 RDs occurring at some point after Nd:YAG capsulotomy. The cumulative risk for retinal tear or detachment at 3, 6, 9, and 12 months was 0.21%, 0.30%, 0.36%, and 0.43% and 0.60%, 0.96%, 1.19%, and 1.39%, respectively. The rates of retinal tear and detachment varied significantly between age categories. CONCLUSIONS: There was an increased risk for RD in the first 5 months after Nd:YAG, with a return to a baseline plateau thereafter. As such, the rate of retinal tear after Nd:YAG capsulotomy at 5 months was 0.29%, whereas the rate of RD was 0.87%.
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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 | 0.001 | 0.002 |
| 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".