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Record W2985558334 · doi:10.6004/jnccn.2019.0039

NCCN Guidelines Insights: Ovarian Cancer, Version 1.2019

2019· article· en· W2985558334 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

VenueJournal of the National Comprehensive Cancer Network · 2019
Typearticle
Languageen
FieldMedicine
TopicOvarian cancer diagnosis and treatment
Canadian institutionsAstraZeneca (Canada)
FundersGenentechPuma BiotechnologyClovis OncologyEisaiNational Comprehensive Cancer NetworkGilead SciencesNovartis Pharmaceuticals CorporationBiogenCelgeneBristol-Myers SquibbAstraZenecaGenomic HealthAmgen
KeywordsMedicineBevacizumabOvarian cancerChemotherapyOncologyCytoreductive surgeryInternal medicineDiseaseCancerHyperthermic intraperitoneal chemotherapyIntensive care medicine

Abstract

fetched live from OpenAlex

Epithelial ovarian cancer is the leading cause of death from gynecologic cancer in the United States, with less than half of patients living >5 years from diagnosis. A major challenge in treating ovarian cancer is that most patients have advanced disease at initial diagnosis. The best outcomes are observed in patients whose primary treatment includes complete resection of all visible disease plus combination platinum-based chemotherapy. Research efforts are focused on primary neoadjuvant treatments that may improve resectability, as well as systemic therapies providing improved long-term survival. These NCCN Guidelines Insights focus on recent updates to neoadjuvant chemotherapy recommendations, including the addition of hyperthermic intraperitoneal chemotherapy, and the role of PARP inhibitors and bevacizumab as maintenance therapy options in select patients who have completed primary 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.

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)0.000
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.283
Threshold uncertainty score0.832

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.053
GPT teacher head0.349
Teacher spread0.296 · 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