Vitamins as Possible Cancer Biomarkers: Significance and Limitations
Bibliographic record
Abstract
The Western-style diet, which is common in developed countries and spreading into developing countries, is unbalanced in many respects. For instance, micronutrients (vitamins A, B complex, C, D, E, and K plus iron, zinc, selenium, and iodine) are generally depleted in Western food (causing what is known as 'hidden hunger'), whereas some others (such as phosphorus) are added beyond the daily allowance. This imbalance in micronutrients can induce cellular damage that can increase the risk of cancer. Interestingly, there is a large body of evidence suggesting a strong correlation between vitamin intake as well as vitamin blood concentrations with the occurrence of certain types of cancer. The direction of association between the concentration of a given vitamin and cancer risk is tumor specific. The present review summarized the literature regarding vitamins and cancer risk to assess whether these could be used as diagnostic or prognostic markers, thus confirming their potential as biomarkers. Despite many studies that highlight the importance of monitoring vitamin blood or tissue concentrations in cancer patients and demonstrate the link between vitamin intake and cancer risk, there is still an urgent need for more data to assess the effectiveness of vitamins as biomarkers in the context of cancer. Therefore, this review aims to provide a solid basis to support further studies on this promising topic.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".