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Record W2791106312 · doi:10.1002/path.5037

Clear cell carcinomas of the ovary and kidney: clarity through genomics

2018· review· en· W2791106312 on OpenAlexafffund
Jennifer X. Ji, Yi Kan Wang, Dawn R. Cochrane, David G. Huntsman

Bibliographic record

VenueThe Journal of Pathology · 2018
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRenal and related cancers
Canadian institutionsBC Cancer AgencyUniversity of British Columbia
FundersBC Cancer AgencyCanadian Cancer Society Research InstituteCanadian Institutes of Health ResearchMichael Smith Health Research BC
KeywordsClear cellPI3K/AKT/mTOR pathwayBiologyCancer researchClear cell renal cell carcinomaClear cell carcinomaCell growthCellChromatinTranscriptomeOvarian cancerRenal cell carcinomaCancerPathologyBioinformaticsMedicineCell biologyGene expressionSignal transductionGeneticsGene

Abstract

fetched live from OpenAlex

Clear cell ovarian carcinoma (CCOC) and clear cell renal cell carcinoma (ccRCC) both feature clear cytoplasm, owing to the accumulation of cytoplasmic glycogen. Genomic studies have demonstrated several mutational similarities between these two diseases, including frequent alterations in the chromatin remodelling SWI-SNF and cellular proliferation phosphoinositide 3-kinase-mammalian target of rapamycin pathways, as well as a shared hypoxia-like mRNA expression signature. Although many targeted treatment options have been approved for advanced-stage ccRCC, CCOC patients are still treated with conventional platinum and taxane chemotherapy, to which they are resistant. To determine the extent of similarity between these malignancies, we performed unsupervised clustering of mRNA expression data from these cancers. This review highlights the similarities and differences between these two clear cell carcinomas to facilitate knowledge translation within future research efforts. Copyright © 2018 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

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 score0.739
Threshold uncertainty score0.384

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.022
GPT teacher head0.275
Teacher spread0.253 · 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

Citations53
Published2018
Admission routes2
Has abstractyes

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