Intraoperative optical coherence tomography guided corneal sweeping for removal of remnant Interface fluid during ultra-thin Descemet stripping automated endothelial keratoplasty
Bibliographic record
Abstract
BACKGROUND: Remnant interface fluid following Descemet stripping automated endothelial keratoplasty (DSAEK) is associated with postoperative detachments. The aim of this study was to assess outcomes of intraoperative optical coherence tomography (iOCT) guided meticulous peripheral corneal sweeping for removal of interface fluid during ultra-thin (UT) DSAEK. METHODS: This retrospective study included all eyes underwent iOCT guided UT-DSAEK from October 2016 to February 2018 at the Hanusch Hospital, Vienna, Austria. Peripheral meticulous corneal sweeping was performed to remove excess fluid. Central graft thickness (CGT) was measured prior to surgery, after graft bubbling and after corneal sweeping. Remnant interface fluid rates were compared between eyes that underwent rebubbling and those that did not. RESULTS: Overall, 28 eyes of 28 patients with a mean age of 73.9 ± 10.0 years were included. An iOCT guided meticulous peripheral sweeping was performed in 89.3% (n = 25) of the cases. Following 84% (n = 21) of the peripheral sweeping performed, remnant fluid was no longer identified. Following peripheral sweeping the interface fluid height was reduced from 17.31 ± 15.96 μm to 3.46 ± 9.52 μm (p < 0.001) and CGT was reduced by 7% (p < 0.001). Rebubbling was performed in 17.9% (n = 5) of the cases. The rebubbling group had a greater proportion of patients that had remnant fluid identified with iOCT at the end of surgery despite meticulous peripheral sweeping (60.0% versus 4.4%, p = 0.01). CONCLUSION: The iOCT identified subclinical remnant fluid in nearly 90% of UT-DSAEK cases. An iOCT guided peripheral corneal sweeping led to resolution of interface fluid in a majority of cases. Eyes with persistent remnant fluid despite peripheral corneal sweeping are more likely to require subsequent rebubbling.
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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 | 0.001 | 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) | 0.000 | 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".