Bibliographic record
Abstract
PURPOSE OF REVIEW: To discuss the development of presbyopia-correcting intraocular lenses (IOLs), what we have learned since their introduction a few decades ago, what are the options currently on the market, and where the technology is heading in the future. RECENT FINDINGS: Multifocal and accommodating IOLs have gone through several modifications to improve distance, intermediate and near vision compared to their predecessors. These modifications have also targeted unwanted side-effects such as glare and halos in the multifocal lenses and inconsistent near-vision results in the accommodating IOLs and although the results have improved, they are far from perfect. Therefore, careful patient selection for each of these technologies is crucial for success and patient satisfaction. SUMMARY: Presbyopia correction remains a great challenge in cataract and refractive surgery. In this article, we review the development of presbyopia-correcting IOLs, starting from the simple, two-zone, multifocal, refractive models introduced 2 decades ago, the current Food and Drug Administration (FDA) approved multifocal and accommodating lenses as well as those undergoing FDA trials and take a look into developing technologies that may be available to us in the future.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".