Topography-guided laser refractive surgery
Bibliographic record
Abstract
PURPOSE OF REVIEW: Topography-guided laser refractive surgery regularizes the front corneal surface irregularities to achieve the desired refractive outcome. This is particularly applicable in highly aberrated corneas, where wavefront aberrometry is often not possible. This article aims to review the recently published results of topography-guided ablations in normal regular corneas, highly aberrated corneas, and its application in conjunction with collagen cross-linking (CXL) in cases of keratectasia. RECENT FINDINGS: Topography-guided laser ablation is increasingly used with good efficacy and safety outcomes in highly aberrated corneas with irregular astigmatism. These include eyes with refractive surgery complications including postlaser in-situ keratomileusis ectasia, decentered ablation, small optical zones, asymmetrical astigmatism, and postradial keratectomy astigmatism. Further indications are for postkeratoplasty astigmatism and keratoconus. Simultaneous topography-guided ablations with CXL in keratectasia have been promising, both in addressing the surface irregularities and progressive nature of the conditions. SUMMARY: Topography-guided laser refractive surgery is proving to be effective and well tolerated in the visual rehabilitation of highly aberrated eyes, with increasing predictability based on the recent research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.005 | 0.003 |
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".