Optimization of femtosecond laser–constructed clear corneal wound sealability for cataract surgery
Bibliographic record
Abstract
PURPOSE: To compare the sealability of femtosecond laser (FSL)-assisted corneal incisions (CIs) with that of triplanar manual (M)-CIs and to determine FSL wound parameters minimizing leakage. SETTING: Private practice. DESIGN: Phase IV, single-surgeon, retrospective cohort study. METHODS: One eye per patient was included. Two groups defined by the main wound (FSL-CI or M-CI) were compared for leakage, inferred by placement of a suture at the end of surgery. Leakage in FSL-CIs was analyzed as a function of customizable wound parameters: anterior plane depth (APD), posterior plane depth (PPD), anterior side-cut angle (ASCA), and posterior side-cut angle (PSCA). The risk of leakage of FSL-CIs with optimal and nonoptimal parameters was further compared with that of M-CIs. RESULTS: A total of 1100 eyes (757 [68.8%] FSL-CI; 343 [31.2%] M-CI) were included. Wound leakage occurred in 133 FSL-CI (17.6%) and 30 M-CI eyes (8.7%) (P < .001). FSL wound parameters associated with the lowest risk of leakage were 60% APD, 70% PPD, 120 degrees ASCA, and 70 degrees PSCA. FSL-CIs constructed with at least 3 optimal parameters (60% APD, 70% PPD, and 120 degrees ASCA) had a similar risk of leakage to M-CIs (odds ratio [OR], 1.1; 95% CI, 0.5-2.3). FSL-CIs with suboptimal parameters had twice the risk of leakage of M-CIs (OR, 2.0; 95% CI, 1.1-3.8). CONCLUSIONS: Overall, FSL-CIs leaked more than M-CIs. However, FSL-CIs with optimized wound profiles had an equivalent risk of leakage to M-CIs. Wound parameter customization is an asset of FSL technology that allows optimization of FSL-CI sealability.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".