The Influence of Blue-Filtering Intraocular Lenses Implant on Exudative Age-Related Macular Degeneration: A Case–Control Study
Bibliographic record
Abstract
PURPOSE: To determine whether the use of a blue light-filtering intraocular lens (IOL) prevents the onset of wet age-related macular degeneration (AMD). More precisely, we examined the proportion of blue light-filtering IOL in a wet AMD patients' sample and compared it with a general North American pseudophakic population sample. DESIGN: Retrospective case-control study. METHODS: Case patients were diagnosed and treated for wet AMD and had prior IOL implantation at least 3 years before the diagnosis of wet AMD. Control patients were randomly selected among patients who had cataract surgery at our institution. They were exempt of AMD and paired for the year of surgery, sex and age at cataract surgery. A total of 196 patients were included in each study group. RESULTS: Among patients with wet AMD, 62.8% had a blue light-filtering IOL compared with 63.3% among control patients (p = 0.92). Mean time between implantation and injection of anti-VEGF in AMD patients was 6.62 years (95% confidence interval (CI): 6.04-7.19) in non-blue light-filtering IOL group and 5.76 years (95% CI: 5.41-6.11) in blue light-filtering IOL group (p = 0.0120). CONCLUSION: No correlations could be established between the presence of a blue light filter in the IOL and the occurrence of wet AMD. AMD patients without blue light-filtering IOL were injected significantly later than patients with an IOL filtering blue light, which contradict the potential clinical benefit of the blue light filter.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".