Retrospective Contralateral Study Comparing Descemet Stripping Automated Endothelial Keratoplasty With Penetrating Keratoplasty
Bibliographic record
Abstract
PURPOSE: To compare the visual outcomes and complications rate after penetrating keratoplasty (PKP) and Descemet stripping automated endothelial keratoplasty (DSAEK), in the fellow eye of the same subjects, and to evaluate the patient's perspective on these operations. METHODS: A retrospective cohort study was undertaken in the Cornea Clinic at the Toronto Western Hospital. We reviewed the records of 12 patients (24 eyes) who underwent PKP in one eye and DSAEK surgery in their fellow eye. Patient's satisfaction for both procedures was evaluated using a subjective questionnaire. These techniques were compared for intraoperative and postoperative complications and visual and refractive outcomes including contrast acuity, contrast threshold, and high-order ocular aberrations (HOA). RESULTS: All the patients in this study preferred the DSAEK operation. They reported faster recovery time [1.5 week in the DSAEK vs 5.3 weeks in the PKP operation (P = 0.01)], significantly less pain, and better visual outcomes with the DSAEK operation. Uncorrected visual acuity and best-corrected visual acuity were significantly better in the DSAEK operated eyes. The DSAEK surgery was associated with significantly less astigmatism (P = 0.0003) and ametropia. Contrast acuity was significantly better in the eye that underwent DSAEK procedure (P < 0.05), whereas contrast threshold was better in the PKP eye. The PKP operated eyes demonstrated increased level of HOA. CONCLUSIONS: Patients preferred the DSAEK operation compared with PKP. Better uncorrected visual acuity, best-corrected visual acuity, and contrast acuity together with avoidance of surgery-induced astigmatism and HOA are the main benefits of the DSAEK technique.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".