Current Surgical Options in the Management of Cataract in Keratoconus Patients
Bibliographic record
Abstract
Abstract An ectatic cornea presents unique challenges at the time of cataract surgery. Keratoconus is the most common corneal ectasia, affecting 2–54.5 per 100,000 people. Cataract surgery in keratoconus often yields suboptimal visual outcomes, with < 50% achieving within +/−0.5D of the desired refractive target in mild–moderate disease and worsening to 0%–18% in severe disease. Improving postsurgical visual outcomes requires a multifaceted approach, starting with modification of risk factors and patient education. Disease progression should be halted prior to any final surgical plan, often requiring the use of corneal crosslinking to create long-term stability in keratometry prior to cataract surgery. Inaccurate keratometry is a common and significant source of postoperative refractive error, and recent developments in keratoconus-specific intraocular lens (IOL) power calculations have attempted to address this. Despite the above strategies, correction of postsurgical refractive error may be done using corrective lenses or IOL-based techniques. Additionally, surgical strategies such as topography-guided photorefractive keratectomy, intrastromal ring segments, or corneal allogenic intrastromal ring segments can be performed pre- or postoperatively to enhance vision. This review seeks to provide an overview of the considerations and available strategies for cataract surgery in patients with keratoconus.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".