A Rescue Technique for Vertical Diplopia After Toric Lens Use in Patients With Keratoconus
Bibliographic record
Abstract
PURPOSE: To describe a patient with irregular astigmatism who developed highly symptomatic monocular vertical diplopia after receiving a toric intraocular lens (IOL) at the time of surgery. METHODS: Data collected for this case report included pre-operative and postoperative uncorrected distance visual acuity (UDVA), corrected distance visual acuity, refraction, mean keratometry, topographic astigmatism, and pachymetry. RESULTS: The patient's vertical diplopia was corrected safely. UDVA improved from 20/40 to 20/20, refraction improved from −0.50 −1.00 × 155° to −1.00 −0.87 × 160°, and mean keratometry changed from 45.18 to 45.08 diopters (D). Topographic astigmatism changed from −1.02 D @ 165° to −1.30 D @ 170°, and central pachymetry decreased from 506 to 491 µm. CONCLUSIONS: Monocular vertical diplopia after toric lens implantation in a patient with keratoconus may be corrected with lens exchange for a non-toric lens and subsequent SmartSurfACE photorefractive keratectomy (SCHWIND eyetech-solutions). [ Journal of Refractive Surgery Case Reports. 2022;2(3):e60–e62.]
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".