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Record W2955997569 · doi:10.1093/jjco/hyz085

An umbrella study of biomarker-driven targeted therapy in patients with platinum-resistant recurrent ovarian cancer: a Korean Gynecologic Oncology Group study (KGOG 3045), AMBITION

2019· article· en· W2955997569 on OpenAlexaff
Jung‐Yun Lee, Ju Yeon Yi, Hyun Soo Kim, June Lim, Sunghoon Kim, Byoung Ho Nam, Hee Seung Kim, Jae‐Weon Kim, Chel Hun Choi, Byoung‐Gie Kim

Bibliographic record

VenueJapanese Journal of Clinical Oncology · 2019
Typearticle
Languageen
FieldMedicine
TopicPARP inhibition in cancer therapy
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMedicineDurvalumabOlaparibOncologyGynecologic oncologyInternal medicineClinical endpointOvarian cancerBiomarkerResponse Evaluation Criteria in Solid TumorsImmunotherapyChemotherapyTargeted therapyClinical trialCancerNivolumabPhases of clinical research

Abstract

fetched live from OpenAlex

A pilot study of biomarker-driven targeted therapy in patients with platinum-resistant recurrent ovarian cancer has been started in Korea. Archival tumor samples were tested for HRD and PD-L1 status. Treatment arms will be allocated according to the test results. For HRD+ patients, we tested the synergistic effects of olaparib and other agents; treatment arms will randomly be allocated. (Arm 1: olaparib and cediranib; Arm 2: olaparib and durvalumab). For HRD- patients, we tested the role of biomarker-driven immunotherapy according to PD-L1 expression (Arm 3: durvalumab and chemotherapy in patients with high PD-L1 expression; Arm 4: durvalumab, tremelimumab, and chemotherapy in patients with low PD-L1 expression). Sixty-eight patients will be included from three Korean institutions within 1 year. The primary endpoint is the response rate according to RECIST 1.1 (6 months after treatment initiation). This trial has been registered with clinicaltrials.gov, and the registration number is NCT03699449.

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)0.004
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.193
Threshold uncertainty score0.944

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.088
GPT teacher head0.440
Teacher spread0.352 · 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

Citations18
Published2019
Admission routes1
Has abstractyes

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