Associations between Lutein, Zeaxanthin, and Age-Related Macular Degeneration: An Overview
Bibliographic record
Abstract
Age-related macular degeneration, the leading cause of blindness in the elderly, is a degenerative condition of the macula characterized by death or dysfunction of the photoreceptors. With the aging population growing, the incidence of age-related macular degeneration is expected to increase. This raises concern about the future of visual dysfunction related falls and the resulting injuries in the elderly population. Lutein and zeaxanthin are macular pigments that may play a role in reducing the development and progression of age-related macular degeneration. Evidence is accumulating on the consumption of lutein and zeaxanthin (in whole food or supplemental form), the resulting concentrations in the serum, and tissue distribution throughout the body, particularly in the retina. Lutein and zeaxanthin intake increases serum concentrations which in turn increases macular pigment density. Existing literature focuses on factors affecting macular pigment density, functions of lutein and zeaxanthin as blue-light filters and antioxidants, and risk factors associated with age-related macular degeneration. Few studies have focused on the impact of dietary lutein and zeaxanthin on retinal function and the potential to preserve vision and prevent further degeneration. This presents an opportunity for further research to determine an effective dose that delays the progression of age-related macular degeneration.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.003 | 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".