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Record W4247607096 · doi:10.1200/edbk_158675

Paradigm Shift in the Management Strategy for Epithelial Ovarian Cancer

2016· review· en· W4247607096 on OpenAlexaff
Keiichi Fujiwara, Jessica N. McAlpine, Stéphanie Lheureux, Noriomi Matsumura, Amit M. Oza

Bibliographic record

VenueAmerican Society of Clinical Oncology Educational Book · 2016
Typereview
Languageen
FieldMedicine
TopicOvarian cancer diagnosis and treatment
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsOvarian cancerSerous fluidClear cellFallopian tubeEndometriosisCancer researchEpithelial ovarian cancerSerous carcinomaEpitheliumPathogenesisCancerOvarian carcinomaMedicineBiologyOncologyPathologyCarcinomaInternal medicineGynecology

Abstract

fetched live from OpenAlex

The hypothesis on the pathogenesis of epithelial ovarian cancer continues to evolve. Although epithelial ovarian cancer had been assumed to arise from the coelomic epithelium of the ovarian surface, it is now becoming clearer that the majority of serous carcinomas arise from epithelium of the distal fallopian tube, whereas clear cell and endometrioid cancers arise from endometriosis. Molecular and genomic characteristics of epithelial ovarian cancer have been extensively investigated. Our understanding of pathogenesis of the various histologic types of ovarian cancer have begun to inform changes to the strategies for management of epithelial ovarian cancer, which represent a paradigm shift not only for treatment but also for prevention, which previously had not been considered achievable. In this article, we will discuss novel attempts at the prevention of high-grade serous ovarian cancer and treatment strategies for two distinct entities in epithelial ovarian cancer: low-grade serous and clear cell ovarian carcinomas, which are relatively rare and resistant to conventional chemotherapy.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.939
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.164
GPT teacher head0.524
Teacher spread0.360 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations11
Published2016
Admission routes1
Has abstractyes

Explore more

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