Posterior lamellar keratoplasty—comparison of deep lamellar endothelial keratoplasty and Descemet stripping automated endothelial keratoplasty in the same patients: a patient’s perspective
Bibliographic record
Abstract
AIM: To evaluate patients' perspectives on endothelial keratoplasty and to compare the outcomes of deep lamellar endothelial keratoplasty (DLEK) and Descemet stripping automated endothelial keratoplasty (DSAEK), performed in the same patients. METHODS: A fellow eye, comparative retrospective case series. The records of 14 patients (28 eyes) who underwent DLEK in one eye and DSAEK surgery in their fellow eye between 2003 and 2007 were reviewed. Two patients were excluded from the study. Both these techniques were compared for intra- and postoperative complications, visual and refractive outcomes including higher-order ocular aberrations (HOA). Patient satisfaction for both procedures was prospectively evaluated using a subjective questionnaire. RESULTS: Nine (75%) of the 12 patients perceived better vision in the DSAEK operated eye. Eight (66.6%) of the patients reported faster recovery following DSAEK. Ten (83%) of them preferred the outcomes of the DSAEK surgery. The intra- and postoperative complications were comparable between both procedures. There was no significant difference in visual outcomes between the procedures. However, the DLEK procedure was associated with a significantly higher degree (p<0.05) of HOA. Endothelial cell loss was similar following DLEK and DSAEK. CONCLUSIONS: We conclude that most patients prefer the DSAEK operation, although there are no differences in visual outcomes between DLEK and DSAEK. Avoidance of surgery-induced hyperopia and HOA is the main benefit 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".