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Record W2898127005 · doi:10.1093/annonc/mdy285.155

Subgroup analysis of rucaparib in platinum-sensitive recurrent ovarian carcinoma: Effect of prior chemotherapy regimens in ARIEL3

2018· article· en· W2898127005 on OpenAlexaff
Domenica Lorusso, Robert L. Coleman, Amit M. Oza, Carol Aghajanian, Ana Oaknin, Andrew Dean, Nicoletta Colombo, J. Weberpals, Andrew R. Clamp, Giovanni Scambia, Alexandra Léary, R. W. Holloway, Margarita Amenedo, Peter C.C. Fong, Jeffrey C. Goh, David M. O’Malley, Susana Banerjee, Sandra Goble, T. Cameron, Jonathan A. Ledermann

Bibliographic record

VenueAnnals of Oncology · 2018
Typearticle
Languageen
FieldMedicine
TopicPARP inhibition in cancer therapy
Canadian institutionsPrincess Margaret Cancer CentreOttawa HospitalUniversity Health Network
Fundersnot available
KeywordsMedicineInternal medicineOncologyChemotherapyPlaceboPopulationAdverse effectBRCA mutationChemotherapy regimenClinical endpointSubgroup analysisRandomized controlled trialCancerOvarian cancerConfidence intervalPathology

Abstract

fetched live from OpenAlex

Background: In the randomised, placebo-controlled, phase 3 study ARIEL3, patients were randomised 2:1 to oral rucaparib (600mg BID) or placebo as maintenance treatment following response to platinum-based chemotherapy. Rucaparib significantly improved progression-free survival (PFS) vs placebo in all patient populations regardless of biomarker status (Coleman et al. Lancet. 2017;390:1949-61). This post hoc exploratory analysis investigated the effect of the number of prior chemotherapy regimens on the primary and secondary endpoints of investigator-assessed and blinded independent central review (BICR)-assessed PFS in ARIEL3.

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)0.012
metaresearch head score (Gemma)0.015
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 score0.012
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.014
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.049
GPT teacher head0.399
Teacher spread0.350 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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