The Effect of Successful Rebubbling After Descemet Stripping Automated Endothelial Keratoplasty on Endothelial Cell Counts
Bibliographic record
Abstract
PURPOSE: To examine the effect of successful repositioning/rebubbling of the graft on endothelial cell counts (ECCs) in eyes after Descemet stripping automated endothelial keratoplasty. METHODS: This retrospective study reviewed the outcomes of 58 eyes that underwent Descemet stripping automated endothelial keratoplasty. Fifty-one eyes were attached after surgery. Seven were detached and underwent rebubbling with minimal manipulation in 5 and significant manipulation in 2, after which the graft was attached and clear. Visual outcomes and endothelial cell loss at 6 months were compared between eyes that underwent repositioning/rebubbling and those that did not. The following were excluded: 2 eyes that had primary failure, 2 eyes that had rejection, 1 eye that failed to attach after rebubbling, 2 eyes that failed to clear after rebubbling, and 24 eyes that did not have ECC at 6 months. RESULTS: The 2 groups were comparable considering age, preoperative best-corrected visual acuity, surgical variables, combined procedures, donor cell count, and graft size. The mean postoperative spherical equivalent, manifest and topographic astigmatism, uncorrected visual acuity, and best-corrected visual acuity did not differ significantly between the 2 groups at 6 months. Mean pre- and postoperative ECC did not differ significantly between the group that underwent repositioning/rebubbling and the group that did not (preoperative counts 2742 +/- 268 vs. 2747 +/- 353 and postoperative counts 1590 +/- 367 vs. 1746 +/- 491, respectively). Endothelial cell loss also did not differ significantly between the 2 groups, although there was a trend to greater cell loss in the reattachment group (-39.4% +/- 11.1% vs. -38.3% +/- 16.2%, respectively; P = 0.6). CONCLUSION: Successful reattachment procedure does not seem to cause significant endothelial cell loss.
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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 | 0.004 |
| 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".