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Record W2974458703 · doi:10.2217/epi-2019-0236

Neuroendocrine Prostate Cancer: Long Noncoding RNAs to Treat an Incurable Cancer – an Interview with Dr Francesco Crea

2019· article· en· W2974458703 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueEpigenomics · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer-related molecular mechanisms research
Canadian institutionsnot available
FundersCancer Research UK
KeywordsProstate cancerCancerEpigeneticsBiologyMetastasisOncologyCancer researchInternal medicineMedicineGeneticsGene

Abstract

fetched live from OpenAlex

Francesco Crea speaks to Lucy Chard, Commissioning Editor. Dr Crea’s lab studies the role of epigenetic factors and noncoding RNA in cancer initiation and progression. While working at the National Cancer Institute (USA), Dr Crea has demonstrated that polycomb-targeting drugs eradicate prostate cancer stem cells. While working at the BC Cancer Agency (Canada), Dr Crea discovered and patented PCAT18, a long noncoding RNA involved in prostate cancer metastasis. Dr Crea has received awards from the American Society of Clinical Oncology, from the Prostate Cancer Program and from Prostate Cancer Foundation BC. He is also an Editorial Board member for Epigenomics. His team is currently working on developing new biomarkers and therapeutic targets for incurable prostate and breast cancers.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.016
GPT teacher head0.304
Teacher spread0.288 · 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