Descemet Stripping Automated Endothelial Keratoplasty Using Infant Donor Tissue
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to report the outcomes of Descemet stripping automated endothelial keratoplasty (DSAEK) surgery using infant (2 years and younger) donor tissue. METHODS: Retrospective interventional case series of 3 patients. RESULTS: All 3 patients in this series had good visual outcomes and clear DSAEK grafts. The average endothelial cell count (ECC) from infant donors was very high (4239 cells/mm(2)). Similarly, the average postoperative ECC was also high (3359 cells/mm(2)) with a mean endothelial cell loss of 20.9% at 11-month follow-up. One patient remarkably had an ECC of 4065 cells per square millimeter at 1-year follow-up with a net endothelial cell loss of only 13.3%. No difficulties were noted using infant donor tissue, including the intraoperative use of the Moria microkeratome to prepare the DSAEK donor, insertion of the donor graft, or with air-bubble management. CONCLUSIONS: Using infant donor tissue for DSAEK surgery is safe and may be preferable, particularly for younger patients. The higher preoperative endothelial cell densities in infant donor tissue should improve graft survival and long-term maintenance of corneal transparency provided that surgery-related endothelial cell loss is minimized.
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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 | 0.002 |
| 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".