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Record W2608878607 · doi:10.1016/j.breast.2017.04.007

New agents for endocrine resistance in breast cancer

2017· review· en· W2608878607 on OpenAlexaff
Christian Maurer, Samuel Martel, Dimitrios Zardavas, Michail Ignatiadis

Bibliographic record

VenueThe Breast · 2017
Typereview
Languageen
FieldMedicine
TopicAdvanced Breast Cancer Therapies
Canadian institutionsHôpital Charles-Le MoyneCentre intégré de santé et de services sociaux de Chaudière-AppalachesUniversité de Sherbrooke
Fundersnot available
KeywordsEndocrine systemBreast cancerMedicineDiseaseAcquired resistanceEstrogen receptorOncologyMetastatic breast cancerInternal medicineBlockadeEstrogenCancerBioinformaticsCancer researchReceptorHormoneBiology

Abstract

fetched live from OpenAlex

Estrogen receptor positive (ER+) and HER2-negative (HER2-) breast cancer (BC) is the most common BC subtype, defined by expression of the ER and absence of HER2 amplification. Endocrine treatment (ET), aiming at therapeutic blockade of ER signaling, represents the therapeutic mainstay for patients with both early and advanced disease. Despite its wide therapeutic efficacy, ET fails for a proportion of ER+, HER2- BC patients with early disease who develop endocrine resistance, resulting in disease recurrence. Endocrine resistance occurs almost invariably in patients with metastatic disease. Recently, increasing understanding of the molecular mediators of endocrine resistance has been achieved. This review focuses on the molecular mechanisms mediating endocrine resistance, on molecularly targeted agents to overcome or delay it, and potential predictive biomarkers for accurate patient stratification.

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 score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.003

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 head0.112
GPT teacher head0.435
Teacher spread0.323 · 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

Citations23
Published2017
Admission routes1
Has abstractyes

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