Vitamin E: The Evidence for Multiple Roles in Cancer
Bibliographic record
Abstract
There is accumulating evidence that vitamin E may have different roles in the prevention and treatment of cancer. The purpose of this review is to summarize and evaluate this evidence and to suggest future avenues of research. A comprehensive literature review of vitamin E and cancer was conducted. Articles were organized into the following categories: 1) cancer prevention, 2) direct antineoplastic activity, 3) augmentation of chemotherapy effects, and 4) attenuation or treatment of chemotherapy toxicity. The evidence was systematically evaluated using guidelines developed by the U.S. Preventative Services Task Force. There is evidence to suggest that those individuals with higher serum vitamin E levels and those receiving vitamin E supplementation have a decreased risk of some cancers, including lung, prostate, stomach, and gastrointestinal carcinoma. However, these results differed depending on the study design and the population studied. There is insufficient evidence to support anticancer activity and attenuation of chemotherapy toxicity by vitamin E. Vitamin E is likely to be important in the prevention of some cancers. The therapeutic role of vitamin E is poorly understood. Further research will be required before routine use of vitamin E in patients with cancer can be advocated in the clinical setting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".