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Record W4399614669 · doi:10.1177/17588359241255174

PARP inhibitors in non-ovarian gynecologic cancers

2024· article· en· W4399614669 on OpenAlexaff
Ítalo Fernandes, Rania Chehade, Helen Mackay

Bibliographic record

VenueTherapeutic Advances in Medical Oncology · 2024
Typearticle
Languageen
FieldMedicine
TopicPARP inhibition in cancer therapy
Canadian institutionsSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineOvarian cancerContext (archaeology)OlaparibOncologyPARP inhibitorEndometrial cancerInternal medicinePoly ADP ribose polymeraseCancer researchCancerPolymeraseBiologyGene

Abstract

fetched live from OpenAlex

Poly(ADP-ribose) polymerase (PARP) inhibitors (PARPis) have transformed the treatment of ovarian cancer, particularly benefiting patients whose tumors harbor genomic events that result in impaired homologous recombination (HR) repair. The use of PARPi over recent years has expanded to include subpopulations of patients with breast, pancreatic, and prostate cancers. Their potential to benefit patients with non-ovarian gynecologic cancers is being recognized. This review examines the underlying biological rationale for exploring PARPi in non-ovarian gynecologic cancers. We consider the clinical data and place this in the context of the current treatment landscape. We review the development of PARPi strategies for treating patients with endometrial, cervical, uterine leiomyosarcoma, and vulvar cancers. Furthermore, we discuss future directions and the importance of understanding HR deficiency in the context of each cancer type.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.982
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.021
GPT teacher head0.396
Teacher spread0.375 · 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.

Study designOther design
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

Citations3
Published2024
Admission routes1
Has abstractyes

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