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Record W4376613897 · doi:10.1016/j.esmoop.2023.101392

203P Clinical effectiveness of olaparib in BRCA-mutated, HER2-negative metastatic breast cancer (mBC) by ER expression level: Subgroup analysis from phase IIIb LUCY trial

2023· article· en· W4376613897 on OpenAlexaff
Karen A. Gelmon, PA Fasching, Suzette Delaloge, Y.H. Park, Andrea Eisen, Hugues Bourgeois, Zoe Kemp, Tomasz Jankowski, Joohyuk Sohn, Sercan Aksoy, Constanta Timcheva, TW Park-Simon, A. Antón Torres, Ellie John, Ian Gibson, Natalia Lukashchuk, Katherine Baria, Judith Balmañà

Bibliographic record

VenueESMO Open · 2023
Typearticle
Languageen
FieldMedicine
TopicAdvanced Breast Cancer Therapies
Canadian institutionsSunnybrook Health Science CentreHealth Sciences CentreUniversity of British Columbia
FundersYonsei University College of MedicineMerck Sharp and DohmeFundació Institut de Recerca Hospital Universitari Vall d’HebronUniversitat de BarcelonaMedizinischen Hochschule HannoverHacettepe ÜniversitesiGilead SciencesMerckAstraZeneca
KeywordsMedicineOlaparibInternal medicineOncologyMetastatic breast cancerTaxaneEribulinBreast cancerPopulationVinorelbineCancerChemotherapyCisplatin

Abstract

fetched live from OpenAlex

In the OlympiAD phase III trial, olaparib significantly prolonged progression-free survival (PFS) vs chemotherapy in patients (pts) with germline-BRCA-mutated (gBRCAm), HER2-negative mBC regardless of hormone receptor (HR) status. In a study population that reflects clinical practice, the phase IIIb LUCY trial similarly showed clinical effectiveness of olaparib regardless of HR status. ASCO/CAP guidelines highlight the lack of data on the best treatment approach for breast tumors that are estrogen receptor (ER)-low (1–10% ER+ stained cells).

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)0.003
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.006
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.090
GPT teacher head0.462
Teacher spread0.372 · 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 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

Citations1
Published2023
Admission routes1
Has abstractyes

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