MétaCan
Menu
Back to cohort
Record W3003435212 · doi:10.14740/wjon1242

The Effect of Metformin on the Clinicopathological Features of Breast Cancer With Type 2 Diabetes

2020· article· en· W3003435212 on OpenAlexvenueno aff
Weili Min, Baofeng Wang, Aining Guo, Guochao Mao, Yang Zhao, Shuqun Zhang, Rui He, Yihe Min, Yi‐Hsiang Huang

Bibliographic record

VenueWorld Journal of Oncology · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolism, Diabetes, and Cancer
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMetforminBreast cancerInternal medicineEstrogen receptorProgesterone receptorCancerMetastasisOncologyType 2 Diabetes MellitusImmunohistochemistryDiabetes mellitusEndocrinology

Abstract

fetched live from OpenAlex

BACKGROUND: The present study aimed to review the use of hypoglycemic drugs and clinicopathological data in breast cancer patients with type 2 diabetes mellitus (T2DM), and to investigate the effect of metformin on the clinicopathological features of breast cancer in patient with T2DM. METHODS: hybridization was used to detect HER-2 (+ and ++). RESULTS: The rate of lymph node metastasis and the level of Ki-67/MMP-2 in the metformin group were significantly lower than those in the control group (P < 0.05). The ratio of luminal pattern in metformin group was higher than that in the control group (P < 0.05). However, there were no differences in the parameters of age, duration of diabetes, body mass index, tumor size, histological grade of cancer and clinical pathological features between the two groups. No significant difference was observed in the expressions of ER, PR, HER-2, EGFR, E-cadherin, N-cadherin and the recurrence rate between two groups. CONCLUSIONS: Metformin is associated with luminal breast cancer and can inhibit breast cancer invasion and metastasis in some cases. It may be associated with EMT and is beneficial to the prognosis of breast cancer.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.773
Threshold uncertainty score0.183

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.012
GPT teacher head0.294
Teacher spread0.281 · 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
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

Citations10
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueWorld Journal of OncologySame topicMetabolism, Diabetes, and CancerFrench-language works237,207