Outcomes of Toric Intraocular Lens Implantation After Radial Keratotomy
Bibliographic record
Abstract
Purpose: To report high toric intraocular lens (IOL) implantation in a patient with previous radial keratotomy and significantly flat corneal powers. Methods: Case report. Results: A 64-year-old woman with previous bilateral 8-incision radial keratotomy was referred for cataract surgery. Corrected distance visual acuity was 20/70 in the right eye and counting fingers in the left eye (could not be refracted). The power of the flat meridian was below 30.00 diopters (D) bilaterally, so no IOL power predictions were reported by standard biometric devices. Calculations were run with the American Society of Cataract and Refractive Surgery (ASCRS) IOL Calculator for Eyes with Prior Radial Keratotomy using data from biometry, tomography, and topography. Toric cylinder and meridian were calculated with the ASCRS Barrett Toric Calculator. Given reasonable agreement in corneal cylinder and meridian between imaging modalities, toric IOLs (3.75 D IOL cylinder) were implanted. Postoperatively, spherical equivalent was −0.50 D in the right eye and plano in the left eye. Cylinder was reduced from 3.24 to 1.00 D in the right eye and from 4.13 to 1.00 D in the left eye. Conclusions: Toric IOLs offer an opportunity to reduce postoperative astigmatism in radial keratotomy cases with high cylinder, significantly flat corneal powers, and no classic bowtie pattern on topography. Pre-cataract refractions are useful in these cases after radial keratotomy, because corneal imaging modalities may underestimate astigmatism after radial keratotomy. [ Journal of Refractive Surgery Case Reports. 2022;2(4):e68–e71.]
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".