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Record W2229177283 · doi:10.1136/jclinpath-2015-203012

Association between androgen receptor expression, Ki-67 and the 21-gene recurrence score in non-metastatic, lymph node-negative, estrogen receptor-positive and HER2-negative breast cancer

2015· article· en· W2229177283 on OpenAlexaff
Francisco Vera-Badillo, Martin C. Chang, Gordana Kuruzar, Alberto Ocaña, Arnoud J. Templeton, Boštjan Šeruga, Robyn Goldstein, Philippe L. Bédard, Ian F. Tannock, Eitan Amir

Bibliographic record

VenueJournal of Clinical Pathology · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsMount Sinai HospitalPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsAndrogen receptorBreast cancerEstrogen receptorProgesterone receptorMedicineInternal medicineOncologyLymph nodeEstrogenCancerKi-67ImmunohistochemistryProstate cancer

Abstract

fetched live from OpenAlex

BACKGROUND: The mechanisms underlying the favourable prognosis of androgen receptor (AR) expression in breast cancer are unknown. METHODS: The associations between the 21-gene recurrence score (RS), AR, grade, mitotic score, Ki-67 and estrogen receptor (ER) and progesterone receptor (PgR) expression were explored in sequential women with lymph node-negative, ER-positive and HER2-negative breast cancer. Statistical significance of this exploratory study was defined as p<0.10. RESULTS: Analysis comprised 70 women. Most tumours had high AR expression (97% had scores >3). Median RS was 15 (range 1-53). AR expression showed a minimally significant positive correlation with ER (R=0.37), but no correlation with Ki-67 (R=-0.18). In univariable analysis, AR (p=0.01), ER (p<0.001) and PgR (p<0.001) had significant negative associations with RS. Ki-67 (p=0.16), grade (p=0.40) and mitotic score (p=0.23) showed no association with RS. Multivariable analysis showed similar associations. CONCLUSIONS: AR is associated with lower RS, but not with Ki-67.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.603

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.031
GPT teacher head0.343
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations10
Published2015
Admission routes1
Has abstractyes

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