Scleral surgery for the treatment of presbyopia: where are we today?
Bibliographic record
Abstract
Presbyopia corrections traditionally have been approached with attempts to exchange power, either at the cornea or the lens planes, inducing multifocality, or altering asphericity to impact the optical system. Treatments that affect the visual axis, such as spectacle and contact lens correction, refractive surgeries, corneal onlays and inlays, and intraocular lenses are typically unable to restore true accommodation to the presbyopic eye. Their aim is instead to enhance 'pseudoaccommodation' by facilitating an extended depth-of-focus for which vision is sufficient. There is a true lack of technology that approaches presbyopia from a treatment based or therapy based solution, rather than a 'vision correction' solution that compromises other components of the optical system. Scleral surgical procedures seek to restore true accommodation combined with pseudoaccommodation and have several advantages over other more invasive options to treat presbyopia. While the theoretical justification of scleral surgical procedures remains controversial, there has nevertheless been increasing interest and evidence to support scleral surgical and therapeutic approaches to treat presbyopia. Enormous progress in scleral surgery techniques and understanding of the mechanisms of action have been achieved since the 1970s, and this remains an active area of research. In this article, we discuss the historic scleral surgical procedures, the two scleral procedures currently available, as well as an outlook of the future for the scleral surgical space for treating presbyopia.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".