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Record W4386996078 · doi:10.3390/cancers15194702

The Potential of Hormonal Therapies for Treatment of Triple-Negative Breast Cancer

2023· review· en· W4386996078 on OpenAlexafffund
Melanie Kirkby, Alyanna Popatia, Jessie R. Lavoie, Lisheng Wang

Bibliographic record

VenueCancers · 2023
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEstrogen and related hormone effects
Canadian institutionsOttawa HospitalInstitute of Infection and ImmunityUniversity of OttawaHealth Canada
FundersGovernment of CanadaCanadian Institutes of Health ResearchCancer Research SocietyUniversity of Ottawa
KeywordsTriple-negative breast cancerBreast cancerMedicineEstrogen receptorProgesterone receptorOncologyHormone receptorInternal medicineAndrogen receptorEstrogenHormoneCancerReceptorCancer researchProstate cancer

Abstract

fetched live from OpenAlex

Triple-negative breast cancer (TNBC) is considered one of the most aggressive forms of breast cancer with poor survival rates compared to other breast cancer subtypes. TNBC is characterized by the absence of the estrogen receptor alpha, progesterone receptor, and the human epidermal growth factor receptor 2, limiting those viable treatment options available to patients with other breast cancer subtypes. Furthermore, due to the particularly high heterogeneity of TNBC, conventional treatments such as chemotherapy are not universally effective, leading to drug resistance and intolerable side effects. Thus, there is a pressing need to discover new therapies beneficial to TNBC patients. This review highlights current findings regarding the roles of three steroid hormone receptors, estrogen receptor beta, the androgen receptor, and the glucocorticoid receptor, in the progression of TNBC. In addition, we discussed several ongoing and completed clinical trials targeting these hormone receptors in TNBC patients.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.991
Threshold uncertainty score0.526

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.023
GPT teacher head0.327
Teacher spread0.303 · 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
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

Citations25
Published2023
Admission routes2
Has abstractyes

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