Outcomes of Repeat Penetrating Keratoplasty and Risk Factors for Graft Failure
Bibliographic record
Abstract
Purpose. To compare repeat penetrating keratoplasty (PKP) with primary PKP with respect to patient characteristics, survival rates, and risk factors for graft failure. Methods. Retrospective, consecutive, noncomparative case series of 116 patients who underwent repeat PKP and who were identified from a cohort of 696 PKPs performed by one surgeon over a 7.5-year period. Results. Compared with patients who underwent primary PKP, regraft patients were 5 years older, had a higher rate of peripheral anterior synechiae (PAS), were more likely to require intraocular pressure (IOP)–lowering medications prior to surgery, were more likely to develop postoperative corneal neovascularization, were less likely to be phakic, and were more likely to undergo PKP in conjunction with a lens procedure. There was no difference between the two groups with respect to the distribution of original diagnoses leading to PKP and the rate of graft rejection. Two- and 5-year survival rates for repeat PKP were 63.9% and 45.6%, respectively. In a multivariate analysis, the original diagnosis leading to corneal transplantation, the presence of preoperative PAS, intraoperative anterior vitrectomy, and postoperative corneal neovascularization were identified as risk factors for graft failure in patients undergoing a regraft. Conclusions. Patients undergoing PKP for the first and second time share common risk factors for graft failure, namely, the original diagnosis leading to corneal transplantation, the presence of preoperative PAS, and the occurrence of postoperative corneal neovascularization. The difference in graft survival rates between the two groups can be partially explained on the basis of higher rates of the latter two risk factors among regrafts.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".