Ex Vivo Evaluation of a Pressure-Sensitive Device to Aid Big Bubble Intrastromal Dissection in Deep Anterior Lamellar Keratoplasty
Bibliographic record
Abstract
Purpose: To develop and perform ex vivo testing for a device designed for semiquantitative determination of intracorneal dissection depth during big bubble (BB) deep anterior lamellar keratoplasty. Methods: A prototype device connected to a syringe and cannula was designed to determine depth of intrastromal placement based on air rebound pressure emitted by a software controlled generator. Ex vivo testing of the device was conducted on human corneas mounted on an artificial anterior chamber in three experiments: (1) cannula purposely introduced at different depths measured with anterior segment optical coherence tomography, (2) cannula introduced as per the BB technique, and (3) simulation of the BB technique guided by the device. Results: A positive pressure differential and successful BB were observed only when the cannula was positioned within 150 microns from the endothelial plane. In all successful BB cases (21/40), a repeatable increase in tissue rebound pressure was detected, which was not recorded in unsuccessful cases. The device was able to signal to the surgeon correct placement of the cannula (successful BB) in 16 of 17 cases and incorrect placement of the cannula (unsuccessful BB) in 8 of 8 cases (94.1% sensitivity, 100% specificity). Conclusions: In our ex vivo model, this novel medical device could reliably signal cannula positioning in the deep stroma for effective pneumatic dissection and possibly aid technical execution of BB deep anterior lamellar keratoplasty. Translational Relevance: A medical device that standardizes big bubble deep anterior lamellar keratoplasty could increase the overall success rate of the surgical procedure and aid popularization of deep anterior lamellar keratoplasty.
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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 | 0.000 | 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".