Cataract Surgery With Toric Intraocular Lenses Can Optimize Uncorrected Postoperative Visual Acuity in Patients With Marked Corneal Astigmatism
Bibliographic record
Abstract
PURPOSE: To study the change in visual acuity and refraction after cataract surgery using a toric posterior chamber intraocular lens in patients with astigmatism after penetrating keratoplasty. METHODS: A retrospective case note analysis of cataract surgery involving toric lens implants performed at the Norfolk and Norwich University Hospital was conducted. The pre- and postoperative visual acuities and refractions were recorded. RESULTS: Seven consecutive patients are described (5 men and 2 women) with a mean age of 62 years. They all underwent penetrating keratoplasty, and in every case, all sutures were removed (mean, 11.2 months before cataract surgery). A marked improvement in both unaided visual acuity and astigmatism was shown after the procedure. The average preoperative unaided acuity was 6/120 (range, 6/24 to counting fingers) compared with a postoperative unaided visual acuity average of 6/15 (6/9-6/24). The average preoperative cylinder was 10.12 D (range, 3.40-17.89 D); postoperatively, this fell to 2.75 D (range, 0.75-4.25). CONCLUSIONS: Cataract surgery with toric intraocular lenses allows the correction of high degrees of regular corneal astigmatism. We discussed the potential advantages and complications of performing toric lens cataract surgery as a secondary procedure.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".