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Fluvastatin and the Breast Cancer Risk: A Meta-analysis of Observational Studies

2016· article· en· W2732011772 on OpenAlexaboutno aff
Dongmei Liu, Jian Zhang, Wei Zhang, James Lu, Jianlun Han, Guangjun Hao, Sheng-Ming Ye

Bibliographic record

VenueWorld Journal of Traditional Chinese Medicine · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Lipids, and Metabolism
Canadian institutionsnot available
Fundersnot available
KeywordsObservational studyBreast cancerMedicineMeta-analysisOncologyFluvastatinInternal medicineCancer

Abstract

fetched live from OpenAlex

Multiple studies have investigated the associations between fluvatatin and the risk of breast cancer (BC), but their results were conflicting. A meta-analysis of observational studies published regarding this subject was conducted in the present study. It aims to estimate the associations between fluvastatin use and the risk of BC. Pubmed and chinese national knowledge infrastructure (CNKI) database was searched up to January, 2015 to identify eligible observational studies, and the Newcastle-Ottawa Scale (NOS) was used to assess quality of the studies. Pooled relative risk (RR) estimates and 95% confidence intervals (CIs) were calculated (fixed effect model: Mantel-Haenszel). Heterogeneities were evaluated before the calculation. A sensitivity analysis was also conducted. In total, four studies contributed to the analysis. Overall, fluvastatin use negatively correlated with BC risk (RR = 0.74, 95 % CI = 0.58, 0.95). In conclusion, fluvastatin use may reduce the risk of BC, but more research is needed to confirm this finding.

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 imitation

Not 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.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.988
Threshold uncertainty score0.090

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.028
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0120.049
Bibliometrics0.0060.007
Science and technology studies0.0010.000
Scholarly communication0.0030.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.082
GPT teacher head0.334
Teacher spread0.252 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designMeta-analysis
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

Citations1
Published2016
Admission routes1
Has abstractyes

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