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Record W2328772545 · doi:10.1097/mou.0000000000000090

Noncoding RNA in bladder cancer

2014· review· en· W2328772545 on OpenAlexaff
Aidan P. Noon, James W.F. Catto

Bibliographic record

VenueCurrent Opinion in Urology · 2014
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer-related molecular mechanisms research
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsBladder cancermicroRNAMedicineNon-coding RNARNADiseaseCancerLong non-coding RNABioinformaticsComputational biologyGenePathologyInternal medicineBiologyGenetics

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Bladder cancer is a common disease whose natural history can be unpredictable. As such, there is an urgent clinical need to identify biomarkers that will improve the care of patients by allowing a more individualized approach. Noncoding RNAs (ncRNAs) are a recently identified subgroup of RNAs whose mature species are not translated into proteins. Here, we review knowledge of ncRNA in bladder cancer, with a focus upon their role in high-risk nonmuscle invasive tumors. RECENT FINDINGS: There have been a number of articles reporting the ability of microRNAs to help evaluate patients with nonmuscle invasive bladder cancer. New long ncRNA species have been evaluated for the first time in bladder cancer. Competing endogenous RNAs and enhancer RNAs show interesting functional and regulatory effects in other cancers, but have yet to be evaluated in bladder cancer. SUMMARY: Novel RNA species are increasingly being used to help prognosticate patients with bladder cancer and to understand key oncological events in the evolution of this disease. Future work is needed to validate potential clinical utility of the RNA species described.

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)0.000
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 score0.992
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.084
GPT teacher head0.439
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

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

Citations12
Published2014
Admission routes1
Has abstractyes

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