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Record W2139923838 · doi:10.1677/erc.1.01141

Is oestrogen receptor- β a predictor of endocrine therapy responsiveness in human breast cancer?

2006· review· en· W2139923838 on OpenAlexafffund
Leigh C. Murphy, Peter H. Watson

Bibliographic record

VenueEndocrine Related Cancer · 2006
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEstrogen and related hormone effects
Canadian institutionsCancerCare ManitobaUniversity of Manitoba
FundersCanadian Institutes of Health ResearchCanadian Breast Cancer Research AllianceStrongCancerCare Manitoba FoundationU.S. Department of Defense
KeywordsBETA (programming language)Breast cancerEstrogen receptor alphaEstrogen receptor betaEndocrine systemAlpha (finance)Internal medicineEstrogen receptorOncologyOestrogen receptorMedicineEndocrinologyHuman breastReceptorCancerCancer researchHormone

Abstract

fetched live from OpenAlex

The role of oestrogen receptor (ER) beta in human breast cancer remains unclear. However, it is now apparent that when considering ER beta in human breast cancer it is important to recognise two ER beta expressing groups, one in which ER beta is co-expressed with ER alpha and the other where ERbeta is expressed alone. Emerging data support different functions between ER beta when it is expressed alone and when it is co-expressed with ER alpha. With regard to the latter group (ER alpha +/ER beta +), there are now 9 out of 10 retrospective clinical outcome studies published, that support the hypothesis that increased expression of ER beta is associated with increased likelihood of response to endocrine therapy. The data strongly support undertaking prospective studies to determine if the addition of ERbeta to ER alpha is clinically beneficial and whether to include both ER beta and ER alpha when establishing clinically relevant cut-offs for defining ER status.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.017
GPT teacher head0.337
Teacher spread0.321 · 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

Citations73
Published2006
Admission routes2
Has abstractyes

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