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Record W2807968494 · doi:10.3747/co.25.4000

Hormone Receptor–Positive, HER2-Negative Metastatic Breast Cancer: Redrawing the Lines

2018· review· en· W2807968494 on OpenAlexaffvenue
Adriana Reis Brandão Matutino, Anil A. Joy, Christine Brezden‐Masley, S. Chia, Sunil Verma

Bibliographic record

VenueCurrent Oncology · 2018
Typereview
Languageen
FieldMedicine
TopicAdvanced Breast Cancer Therapies
Canadian institutionsBC Cancer FoundationBC Cancer AgencySt. Michael's HospitalUniversity of AlbertaUniversity of Calgary
Fundersnot available
KeywordsMetastatic breast cancerMedicineHormone receptorCancerHER2 negativeCancer researchBreast cancerHormoneReceptorOncologyInternal medicine

Abstract

fetched live from OpenAlex

Estrogen receptor modulators and estrogen deprivation have become standards of care for hormone receptor-positive metastatic breast cancer. However, after traditional first-line endocrine monotherapy treatment, the disease typically progresses despite the initial high rate of clinical benefit. Multiple studies have aimed at optimizing treatment strategies to improve upon clinical benefit beyond the traditional single-agent endocrine treatment. With the availability of new data and novel therapies, the clinical practice challenge becomes how best to define the optimal treatment sequence to maximize clinical benefit. In this review, we present treatment options clinically relevant to the management of hormone-positive, her2-negative metastatic breast cancer, and we propose a treatment algorithm based on the current literature.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.987
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.147
GPT teacher head0.471
Teacher spread0.324 · 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.

Study designOther design
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

Citations48
Published2018
Admission routes2
Has abstractyes

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