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Record W2138368669 · doi:10.2217/fon.15.251

Current State of Biomarkers in Ovarian Cancer Prognosis

2015· review· en· W2138368669 on OpenAlex
Katrina Au, Juliana Alves Josahkian, Julie‐Ann Francis, Jeremy A. Squire, Madhuri Koti

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

VenueFuture Oncology · 2015
Typereview
Languageen
FieldMedicine
TopicOvarian cancer diagnosis and treatment
Canadian institutionsKingston General HospitalQueen's University
Fundersnot available
KeywordsMedicineOvarian cancerCarboplatinPaclitaxelTumor microenvironmentDiseaseDrug resistanceOncologyChemotherapyCancerSerous fluidInternal medicineBioinformaticsCisplatinBiology

Abstract

fetched live from OpenAlex

High-grade serous ovarian cancer remains one of the most lethal malignancies in women. Despite recent advances in surgical and pharmaceutical therapies, survival rates remain poor. A major impediment in management of this disease, that continues to contribute to poor overall survival rates, is resistance to standard carboplatin-paclitaxel combination chemotherapies. In addition to tumor cell intrinsic mechanisms leading to drug resistance, there is increasing awareness of the crucial role of the tumor microenvironment in mediating natural immune defense mechanisms and selective pressures that appear to facilitate chemotherapy sensitivity. We provide an overview of some of the promising new genetic and immunological biomarkers in ovarian cancer and discuss their biology and their likely clinical utility in future ovarian cancer management.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.969
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.075
GPT teacher head0.430
Teacher spread0.355 · 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