Optical Coherence Tomography of Descemet Membrane Separation by the Big Bubble Technique
Bibliographic record
Abstract
PURPOSE: To image Descemet membrane separation by the big bubble technique in human corneas by using anterior segment optical coherence tomography (OCT). METHODS: Five human corneoscleral rims were placed on an artificial anterior chamber and partially trephinated. A 23-gauge needle was inserted into the stroma under slit-lamp control and air was injected. The procedure was continuously imaged by anterior segment OCT. RESULTS: In all corneoscleral rims, a big bubble was created. The spread of air seemed to follow the interlamellar spaces without crossing lamellae. It involved mainly the inner layers of the stroma while sparing the outer 212 +/- 41 microm of the cornea (range, 168-271 microm). Intrastromal pressure build-up forced air above the Descemet membrane, creating tiny air bubbles of approximately 355 +/- 111 microm (range, 210-560 microm). When the pressure inside those bubbles reached a certain level, the bubbles spontaneously coalesced to form a big bubble. CONCLUSIONS: OCT is useful in imaging intracorneal air spread. The main obstacle to creating a big bubble is the impermeability to air of the imperforated posterior stromal lamellae.
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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 | 0.001 |
| 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".