Effect of Corneal Hydration on the Quality of the Femtosecond Laser Anterior Lamellar Cut
Bibliographic record
Abstract
The goal of this study was to assess the effect of corneal hydration on the quality of the femtosecond laser (FSL) anterior lamellar cut. The Visumax FSL was used to dissect an 8-mm-diameter corneal flap in 22 eye bank corneas showing various levels of hydration. The intended ablation depth was 220 µm in all eyes, which corresponded to the maximal depth available with this laser. After the cut, the achieved ablation depth was measured using optical coherence tomography images, flap separability was assessed by measuring the mean force generated to detach the flap, and stromal bed roughness was assessed by measuring the Haralick contrast level on the 1000× scanning electron microscopy images of the ablated surfaces. The preoperative central corneal thickness ranged from 547 to 1104 µm (mean ± SEM: 833 ± 30 µm). A negative correlation was found between the level of corneal hydration and the ablation depth measured in the mid-peripheral cornea (r = -0.626, p = 0.003), the ablation being more superficial in more edematous corneas. The Haralick contrast also tended to increase as a function of corneal hydration (r = 0.416, p = 0.061), suggesting that laser ablation in edematous corneas results in rougher stromal surfaces. These results support the hypothesis that the quality of the FSL lamellar cut decreases as the level of corneal hydration increases. Although FSL is still considered in the field as the tool of the future for corneal dissection, a better understanding of the limits of this tool will be needed before it can replace manual or automated stromal dissection techniques in hydrated corneas.
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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 | 0.002 |
| 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".