Pseudo Corneal Ectasia Due to Corneal Endothelial Disease
Bibliographic record
Abstract
Purpose To describe two patients referred for presumed ectasia after laser in situ keratomileusis (LASIK) based on increasing myopia and corneal steepening years after LASIK. In both patients, further testing confirmed endothelial disease with central corneal edema as the cause of topographic changes. Methods Two patients showing symptoms similar to post-LASIK ectasia were assessed with Scheimpflug imaging (Pentacam; Oculus Optikgeräte GmbH) and subsequently treated with endothelial keratoplasty. Results Case 1 was a 66-year-old woman with increasing myopia after LASIK that was initially diagnosed as post-LASIK ectasia. Pentacam imaging revealed central corneal steep-ening with central corneal edema, and the clinical presence of guttata was consistent with Fuch's endothelial dystrophy. Case 2 was a 49-year-old man who also had increasing myopia and corneal steepening after LASIK, but no guttata. Pentacam imaging showed a similar pattern of central corneal swelling. Both patients underwent endothelial keratoplasty with reversal of corneal steepening. Conclusions Several conditions can mimic corneal ectasia, necessitating thorough clinical examination and tomography. This report describes two cases of central corneal edema resulting in myopia and corneal steepening that mimicked corneal ectasia. [ Journal of Refractive Surgery Case Reports. 2024;4(3):e24–e29.]
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".