Linkage Between a Plant-based Diet and Visual Impairment: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
The purpose of this paper is to characterize the relationship between plant-based diets and specific ocular outcomes among adults. Data Sources: A comprehensive literature review was performed using the databases MEDLINE, EMBASE, Cochrane, and PubMed up until December 19, 2021. Study Selection: Studies that focused on observed visual changes due to a reduced intake of animal products with a minimum of 50 eyes were eligible for inclusion. Data Extraction: Two levels of screening, quality assessment, and data extraction were conducted by two reviewers independently. The twenty-one studies identified from 814 unique studies progressed to data extraction and fifteen were included in the quantitative analysis using STATA 15.0 fixed-effect and random-effect models computed based on heterogeneity. Fifteen studies (51,695 participants) assessed the impact of fish consumption, eight studies (28,753 participants) analyzed the effect of red meat intake, and three studies (7723 participants) assessed the impact of omission of skim milk, poultry, and non-meat animal products and the presence of visual deterioration as indicated by age-related macular degeneration, age-related maculopathy, or cataract development. Meta-analysis indicated regular consumption of fish (OR = 0.70; CI: [0.62–0.79]) and skim milk, poultry, and non-meat animal products (OR = 0.70; CI: [0.61–0.79]) reduced the risk of visual impairment among adults. Consumption of red meat [OR = 1.41; CI: [1.07–1.86]) may increase the risk of visual impairment. A pescatarian diet is associated with the most favorable visual outcomes among adults while consumption of red meat negatively impacts vision. Results suggest a need for more initiatives promoting a healthy and balanced diet. Not applicable.
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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 | 0.017 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.037 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".