Ultrasound-induced corneal incision contracture survey in the United States and Canada
Bibliographic record
Abstract
PURPOSE: To ascertain factors associated with corneal incision contracture (wound burn) secondary to phacoemulsification in the United States and Canada. SETTING: John A. Moran Eye Center, University of Utah, Salt Lake City, Utah, USA, and University of Toronto, Toronto, Ontario, Canada. DESIGN: Cross-sectional study. METHODS: Through state and provincial societies, members were queried as to cataract surgery practices during the previous 3 years as well as the specifics associated with each case of wound burn, if any, encountered during that period. RESULTS: Eight hundred forty-two cataract surgeons reported on 920 095 surgeries and 341 wound burns (raw incidence 0.037%). After a multivariate analysis, the wound burn incidence was significantly inversely associated with the surgeon’s surgical volume (45% decrease per doubling of volume; 95% confidence interval, 38%-55%; P<.001), the surgical approach (P<.001), and the ophthalmic viscosurgical device (OVD) used (P=.004). Machine or ultrasound modality used, region of the U.S. or Canada, and incision size were not related to wound burn. CONCLUSION: Phacoemulsification-induced wound burn can be reduced by experience, by the approach used in nucleus disassembly, by choice of OVD, and most important, by not using ultrasound when the anterior chamber is filled with OVD. Financial Disclosure: Dr. Braga-Mele is a consultant to Abbott Medical Optics, Inc., Alcon Laboratories, Inc., and Bausch & Lomb. Dr. Olson has been a consultant to Abbott Medical Optics, Inc., Becton, Dickinson and Co., and Allergan, Inc. He has received grant support from Abbott Medical Optics, Inc. and Allergan, Inc. No other author has a financial or proprietary interest in any material or method mentioned.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".