Overlooked dietary insufficiencies impacting visual impairment: A systematic review and meta-analysis
Bibliographic record
Abstract
Malnourished individuals are at a higher risk of developing visual impairment. Dietary restrictions can cause nutritional insufficiencies and negatively impact overall health. This review was performed to characterize the correlation between restrictions in dietary intake and various forms of visual impairment (VI) in adults. The CINAHL, EMBASE, MEDLINE, and PubMed databases were systematically searched through July 21, 2022. Studies that investigated observed visual changes due to dietary restrictions and omission of dietary components were eligible for inclusion. Of the 2541 unique studies, 22 eligible studies underwent data extraction, and 11 were incorporated into the quantitative meta-analysis. Meta-analysis identified that an adequate intake of fish (OR = 0.62; CI: [0.49–0.79]), and micronutrients (OR = 0.49; CI: [0.25–0.96]) are positively correlated with a decreased odds of VI as indicated by the presence of age-related macular degeneration, diabetic retinopathy, distance acuity, retinal acuity, age-related maculopathy, cataract development, and dual sensory impairment among adults. Overall, dietary restrictions and picky eating may be associated with unfavorable visual outcomes. These outcomes may reduce quality of life, independence, mobility, and driving ability among adults. Findings suggest the need for initiatives to encourage a healthy and balanced diet. Further education and instruction among healthcare providers might be initiated to allow for recognition of dietary insufficiencies and their associated adverse outcomes in order to reduce the possibility of developing severe and potentially irreversible consequences.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.037 |
| Bibliometrics | 0.007 | 0.008 |
| 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) | 0.004 | 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".