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Record W2048253418 · doi:10.1207/s15327914nc4601_01

Vitamin E: The Evidence for Multiple Roles in Cancer

2003· review· en· W2048253418 on OpenAlexaff
Lillian Sung, Mark Greenberg, Gideon Koren, George Tomlinson, Agnes Tong, David Malkin, Brian M. Feldman

Bibliographic record

VenueNutrition and Cancer · 2003
Typereview
Languageen
FieldMedicine
TopicAntioxidant Activity and Oxidative Stress
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsMedicineCancerChemotherapyVitamin D and neurologyToxicityCancer preventionVitamin EPopulationOncologyVitaminInternal medicinePhysiologyIntensive care medicineEnvironmental healthBiology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.977
Threshold uncertainty score0.449

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.182
GPT teacher head0.446
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations30
Published2003
Admission routes1
Has abstractyes

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