Creating endothelial lenticules with femtoseconde laser: the double layer technique
Bibliographic record
Abstract
Abstract Purpose Femtosecond laser(FS) enhances reproducibility and accuracy in corneal surgery. However, visual outcomes of Femtoseconde Lamellar Endothelial Keratoplasty (FLEK) are still impaired by lenticule irregularities. We aimed to enhance the smoothness of interface of FLEK lenticules. Methods We proceeded for corneal cuts on experimental human corneas with the 60 and 150 kHz Intralase FS (AMO, USA). Laser settings were optimized to obtain the best interface quality while delivering minimal energy to the corneal stroma. We did each procedure in triplicate with the appropiate settings to test reproducibility. We created posterior lenticules for FLEK with the following FS various cut profiles: a single path profile (SP) performing a 500µm deep full lamellar cut, a double path profile (DP) with an identical lamellar cut performed twice, a double layer profile (DL) performing two successive lamellar cuts at 350µm and then at 150 µm depth. We created 100 µm LASIK free flaps as a control. The stromal interface quality of the so‐obtained interfaces was analyzed by scanning electron microscope (SEM). Results Stromal adherences persisted after both the SP and the DP procedure, creating central irregularities on the endothelial lenticule. The DL profile created the smoothest interfaces with the best reproducibility when FS parameters for lamellar cut were set for diameter (mm), depth (µm), energy (µJ), and spot size/step (µm) respectively on 9.0mm, 350µm, 2.1µJ, 4:4µm and 8.3mm, 150µm, 0.9µJ, 4:4µm. Observed with SEM, EK lenticules created with the DL profile and LASIK flap had similarly smooth interfaces. Conclusion Femtosecond lasers can creat EK lenticules with a quality of stromal interface comparable to refractive surgery.
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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 | 0.000 | 0.000 |
| 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) | 0.003 | 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".