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Record W1817947240 · doi:10.3934/genet.2015.4.250

Associations of CYP1A1 gene polymorphisms and risk of breast cancer in Indian women: a meta-analysis

2015· article· en· W1817947240 on OpenAlexaff
Noel Pabalan, Neetu Singh, Eloisa Singian, Caio Parente Barbosa, Bianca Bianco, Hamdi Jarjanazi

Bibliographic record

VenueAIMS Genetics · 2015
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmacogenetics and Drug Metabolism
Canadian institutionsMinistry of the Environment, Conservation and Parks
Fundersnot available
KeywordsMeta-analysisBreast cancerMedicineOncologySubgroup analysisInternal medicineEpidemiologyCase-control studyDemographyPublication biasGynecologyCancer

Abstract

fetched live from OpenAlex

Abstract Reported associations of CYP1A1 polymorphisms with breast cancer have been inconsistent. In this meta-analysis examining breast cancer associations of three CYP1A1 polymorphisms (M1, M2 and M4) among Indian women may yield information that may be of clinical and epidemiological use for this particular demography. We searched MEDLINE using PubMed and Embase for association studies. From seven published case-control studies, we estimated overall associations and applied subgroup analysis to explore differential effects. All three polymorphisms exhibited overall increased risk, significant in M1 (OR 1.61–1.65, p = 0.04) and M4 (OR 2.02–3.92, p = 0.02–0.04). Differential effects were observed only in the M1 polymorphism where M1 effects were significant in South Indians (OR 2.20–4.34, p < 0.0001) but not the North population, who were at reduced risk (OR 0.64–0.77, p = 0.03–0.55). These populations were not materially different in regard to M2 and M4 as did the women stratified by menopausal status. In this meta-analysis, M1 and M4 effects may render Indian women susceptible, but may be limited by heterogeneity of the studies. Differential effects of the M1 polymorphism in breast cancer render South Indians susceptible compared to those in the North.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
Threshold uncertainty score0.817

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.163
GPT teacher head0.427
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 designObservational
Domainnot available
GenreEmpirical

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

Citations3
Published2015
Admission routes1
Has abstractyes

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