MétaCan
Menu
Back to cohort
Record W2463928251 · doi:10.3390/jcm5070065

EMT in Breast Carcinoma—A Review

2016· review· en· W2463928251 on OpenAlexaff
Joema Felipe Lima, Sharon Nofech‐Mozes, Jane Bayani, John M.S. Bartlett

Bibliographic record

VenueJournal of Clinical Medicine · 2016
Typereview
Languageen
FieldMedicine
TopicCancer Cells and Metastasis
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreOntario Institute for Cancer Research
Fundersnot available
KeywordsMedicineMetastasisEpithelial–mesenchymal transitionBreast cancerCarcinogenesisCancer researchCancerBreast carcinomaPhenotypeCarcinomaColonizationOncologyPathologyBioinformaticsInternal medicineBiologyGene

Abstract

fetched live from OpenAlex

The epithelial to mesenchymal transition (EMT) is a cellular program that is involved in embryonic development; wound healing, but also in tumorigenesis. Breast carcinoma (BC) is the most common cancer in women worldwide, and the majority of deaths (90%) are caused by invasion and metastasis. The EMT plays an important role in invasion and subsequent metastasis. Several distinct biological events integrate a cascade that leads not only to a change from an epithelial to mesenchymal phenotype, but allows for detachment, migration, invasion and ultimately, colonization of a second site. Understanding the biological intricacies of the EMT may provide important insights that lead to the development of therapeutic targets in pre-invasive and invasive breast cancer, and could be used as biomarkers identifying tumor subsets with greater chances of recurrence, metastasis and therapeutic resistance leading to death.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.003

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 head0.230
GPT teacher head0.542
Teacher spread0.312 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations235
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Clinical MedicineSame topicCancer Cells and MetastasisFrench-language works237,207