Efficacy and Safety of Iris-Claw Intraocular Lens in Pediatric Ectopia Lentis: A Literature Review
Bibliographic record
Abstract
Purpose: To review current evidence regarding the use of iris-claw intraocular lens (IOL) in terms of its efficacy and safety in the population of pediatric ectopia lentis. Methods: A comprehensive literature search of six electronic databases (PubMed-NCBI, Medline-OVID, Embase, Cochrane, Scopus, and Wiley) and secondary search through reference lists was conducted using keywords selected a priori. All primary studies on the use of iris-claw in pediatric ectopia lentis that evaluated visual acuity (VA), complications, and endothelial cell density (ECD) were included and critically appraised using the Newcastle-Ottawa Scale. Results: Ten studies were eligible for inclusion with an overall sample size of 168 eyes of children with ectopia lentis, and the majority of studies evaluated anterior iris-claw IOL. All studies reported improvement in postoperative VA. The most commonly reported complication across studies was IOL decentration. All studies reported decreasing ECD, and this was observed in both anterior and retropupillary iris-claw IOL. Conclusion: Current evidence shows that iris-claw IOL is effective in terms of improving VA in pediatric ectopia lentis. Due to the lack of long-term evidence of its safety in children, one must remain cautious regarding potential endothelial cell loss. Further high-quality, interventional, long-term studies are needed.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".