Association Between Vitamin Deficiencies and Ophthalmological Conditions
Bibliographic record
Abstract
Austin Pereira,1 R Damilola Adekunle,2,* Michele Zaman,3,* Michael J Wan1 1University of Toronto Department of Ophthalmology & Vision Sciences, Toronto, Ontario, Canada; 2Faculty of Medicine, Memorial University of Newfoundland, St. John’s, Newfoundland, Canada; 3Queen’s School of Medicine, Kingston, Ontario, Canada*These authors contributed equally to this workCorrespondence: Michael J Wan, Hospital for Sick Children, 555 University Avenue, Toronto, Ontario, M5G 1X8, Canada, Tel +1 416-813-1836, Fax +1 416-813-7040, Email michael.wan@sickkids.caAbstract: Vitamin deficiencies can have adverse effects on health, including on the visual system. The ocular manifestations of a vitamin deficiency are related to the underlying biochemical function of the particular nutrient. While vitamin deficiencies are not common in developed counties, they are still prevalent in parts of the developing world and in specific, vulnerable populations. Vitamin deficiencies can cause or contribute to many ophthalmological conditions and eye diseases may even be the first presenting finding of a vitamin deficiency. As such, it is important for ophthalmologists to be aware of the ocular manifestations of vitamin deficiencies, especially given that the complications can be severe and effectively treated if identified early. This review summarizes the literature on the main vitamins known to have characteristic ocular manifestations: vitamins A, B1, B2, B9, B12, C, D, E and K. The function, epidemiology, manifestations, workup, and management of each vitamin is discussed in detail.Keywords: vitamin A deficiency, vitamin B deficiency, vitamin C deficiency, vitamin D deficiency, vitamin E deficiency, vitamin K deficiency
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".