Intraoperative use of spectral-domain optical coherence tomography during Descemet’s stripping automated endothelial keratoplasty
Bibliographic record
Abstract
PURPOSE: To evaluate the intraoperative changes in the donor lenticule, recipient cornea, and the reduction of interface fluid thickness during Descemet's stripping and automated endothelial keratoplasty with EndoGlide™ (Angiotech Pharmaceuticals Inc, Vancouver, Canada) donor insertion, using intraoperative spectral-domain optical coherence tomography. METHODS: Prospective observational case series of patients underwent Descemet's stripping and automated endothelial keratoplasty using the EndoGlide inserter. Spectral-domain optical coherence tomography (iVue; Optovue Inc, Fremont, CA) with a handheld probe was used to image the cornea and anterior chamber. Standardized software was used to measure interface fluid gap, host cornea, and donor lenticule thicknesses during the following surgical stages of Descemet's stripping and automated endothelial keratoplasty: (1) after donor insertion and immediately before full air tamponade; (2) after air tamponade and expression of fluid from venting incisions; (3) at 6 minutes of air tamponade; and (4) at 10 minutes of air tamponade. RESULTS: Ten patients with a mean age of 74.9 ± 11.8 years were recruited. Spectral-domain optical coherence tomography measurements of the interface fluid gap after fluid was expressed through the venting incisions (P < 0.001), at 6 minutes of air tamponade (P < 0.001) and at 10 minutes of air tamponade (P < 0.001 and P = 0.001, respectively), were significantly decreased compared to the measurements immediately before air tamponade. Donor thickness increased significantly at 6 minutes of air tamponade (P = 0.004) but reduced by 10 minutes compared to immediately before air tamponade. CONCLUSION: Significant intraoperative changes in the donor, recipient cornea, and interface fluid thickness occurred following endothelial keratoplasty donor insertion.
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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".