Intraocular Lens Calculation in a Patient With Previous Penetrating Keratoplasty and LASIK
Bibliographic record
Abstract
PURPOSE: To report a case of a patient who underwent cataract extraction with intraocular lens (IOL) implantation after previous penetrating keratoplasty (PK) followed by laser in situ keratomileusis (LASIK). METHODS: Case report and literature review of cataract surgery after PK and LASIK. Cataract surgery was successfully performed in a patient with previous PK and LASIK. This paper outlines our method of calculating the correct power IOL for implant. RESULTS: The patient's 1-month postoperative uncorrected visual acuity was 20/70 and best spectacle-corrected visual acuity was 20/30+ with -0.75 +0.50 x 180. CONCLUSIONS: We report the case of a patient with cataract extraction with IOL implantation after PK and LASIK as well as a description of the method used to calculate IOL power after PK and LASIK. While the IOL selection can be difficult, using the appropriate nomogram can result in good visual outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".