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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 teacher head, 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".