Comparison of ACIOL Exchange and Descemet Membrane Endothelial Keratoplasty with ACIOL Retention and Descemet Stripping Automated Endothelial Keratoplasty in the Setting of Pseudophakic Bullous Keratopathy
Bibliographic record
Abstract
Abstract Objective: To compare the clinical outcomes and complications of anterior chamber intraocular lens (ACIOL) exchange and Descemet membrane endothelial keratoplasty (DMEK) with ACIOL retention and Descemet stripping automated endothelial keratoplasty (DSAEK) in patients with PBK.Methods: A multicenter retrospective cohort study. Patients with ACIOL who underwent endothelial keratoplasty procedure due to PBK between 2012-2018 in two tertiary medical centers, were identified. Clinical and demographical data including preoperative and postoperative characteristics were collected.Results: Thirteen eyes in the “DMEK and ACIOL exchange” group and 15 in the “DSAEK and ACIOL retention” group were included in the analysis. Mean BCVA six months postoperatively was 0.51±0.20 LogMAR (Snellen 20/64) and 0.57±0.22 LogMAR (Snellen 20/83) in the “DMEK and ACIOL exchange” group and “DSAEK and ACIOL retention” group, respectively (P=0.38). Graft failure occurred in 6 eyes (40%) in the “DSAEK and ACIOL retention” group; four of them were secondary failures occurring at an average follow-up time of 15±11.9 months. In the “DMEK and ACIOL exchange” group, graft failure occurred in one eye and was a primary failure (P=0.046). In the “DMEK and ACIOL exchange” group, postoperative complications were seen in 4 eyes (30.7%). No postoperative complications were recorded in the “DSAEK and ACIOL retention” group (P=0.035).Conclusion: Despite the lower complication rate, the higher incidence of graft failure and the need for second keratoplasty in the DSAEK group along with the similar visual outcomes, might suggest that in the indication of PBK, ACIOL exchange with DMEK offers a good alternative to ACIOL retention with DSAEK.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".