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Record W4323024632 · doi:10.1177/17588359231157644

Fighting resistance: post-PARP inhibitor treatment strategies in ovarian cancer

2023· review· en· W4323024632 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueTherapeutic Advances in Medical Oncology · 2023
Typereview
Languageen
FieldMedicine
TopicPARP inhibition in cancer therapy
Canadian institutionsBC Cancer AgencyPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMedicinePARP inhibitorOvarian cancerOncologyCancer researchPoly ADP ribose polymeraseInternal medicineCancerPharmacologyDNABiologyGenetics

Abstract

fetched live from OpenAlex

Poly (ADP-ribose) polymerase inhibitors (PARPis) represent a therapeutic milestone in the management of epithelial ovarian cancer. The concept of 'synthetic lethality' is exploited by PARPi in tumors with defects in DNA repair pathways, particularly homologous recombination deficiency. The use of PARPis has been increasing since its approval as maintenance therapy, particularly in the first-line setting. Therefore, resistance to PARPi is an emerging issue in clinical practice. It brings an urgent need to elucidate and identify the mechanisms of PARPi resistance. Ongoing studies address this challenge and investigate potential therapeutic strategies to prevent, overcome, or re-sensitize tumor cells to PARPi. This review aims to summarize the mechanisms of resistance to PARPi, discuss emerging strategies to treat patients post-PARPi progression, and discuss potential biomarkers of resistance.

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.

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 categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.988
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
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 head0.091
GPT teacher head0.485
Teacher spread0.394 · 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