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Record W2126161148 · doi:10.1017/s0007114509991437

<i>Trans</i>-fatty acids and cancer: a mini-review

2009· review· en· W2126161148 on OpenAlexafffund
Brennan K. Smith, Lindsay E. Robinson, Robert K. Nam, David W.L.

Bibliographic record

VenueBritish Journal Of Nutrition · 2009
Typereview
Languageen
FieldNursing
TopicFatty Acid Research and Health
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsProstate cancerBreast cancerDiseaseColorectal cancerCancerHuman healthBioinformaticsHuman studiesAffect (linguistics)BiologyMedicineGeneticsPhysiologyInternal medicinePsychologyEnvironmental health

Abstract

fetched live from OpenAlex

The association between trans-fatty acids (TFA) and cancer risk is poorly understood and remains controversial. It is recognised that unique biological effects are associated with specific isoforms within families of fatty acids such as those belonging to the n-3 fatty acids. Furthermore, the interactions between diet and genetic polymorphisms are increasingly recognised for their potential risk-modifying effects on human health and disease. Therefore, the aim of the present review is to evaluate whether specific TFA isomers and genetic polymorphisms differentially modify cancer risk in prostate, colon and breast cancers in animal and human models. Potential mechanisms of action by which TFA may affect cancer development are also reviewed. Overall, across a number of experimental models and human studies, there is insufficient and inconsistent evidence linking specific TFA isomers to cancers of the prostate, colon and breast. A number of methodological limitations and experimental considerations were identified which may explain the inconsistencies observed across these studies. Therefore, further research is warranted to accurately assess the relationship between TFA and cancer risk.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.806
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.052
GPT teacher head0.377
Teacher spread0.325 · 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.

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

Citations54
Published2009
Admission routes2
Has abstractyes

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