Optimal timing of capsular tension ring implantation: Miyake-Apple video analysis
Bibliographic record
Abstract
PURPOSE: To evaluate the appropriate timing for capsular tension ring implantation in cases of zonular weakness either prior to or after lens extraction using Miyake-Apple video analysis. SETTING: John Moran Eye Center, Salt Lake City, Utah, USA. METHODS: Four cadaver eyes were prepared using a standard Miyake-Apple protocol with image capture using digital video recording. After continuous curvilinear capsulorhexis and hydrodissection/viscodissection were performed, 2 eyes had early capsular tension ring implantation (CTR) and 2 eyes had CTR implantation after lens extraction. The 12.3 mm CTR was implanted in all eyes. Capsular bag torque and displacement, zonular elongation and stress, and ease of CTR placement were evaluated in each eye. RESULTS: Early CTR implantation resulted in significantly increased capsular torque and displacement of up to 4.0 mm compared to insertion in an empty capsular bag. There was significant zonular elongation and tension during early placement. CONCLUSION: In terms of minimizing further zonular stress and damage and capsular destabilization, the ideal timing for CTR placement is after lens extraction and decompression of the capsular bag.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".