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Record W2997456566 · doi:10.21037/tau.2019.11.25

Urinary biomarkers in prostate cancer: to the miRnome and beyond

2020· letter· en· W2997456566 on OpenAlexafffund
Christianne Hoey, Renu Jeyapala, Paul C. Boutros, Bharati Bapat, Stanley K. Liu

Bibliographic record

VenueTranslational Andrology and Urology · 2020
Typeletter
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsSinai Health SystemLunenfeld-Tanenbaum Research InstituteUniversity of TorontoSunnybrook Health Science Centre
FundersNational Cancer InstituteNational Institutes of HealthProstate Cancer CanadaMovember Foundation
KeywordsProstate cancerBiomarkerTranscriptomeUrinary systemMedicineBiomarker discoveryInternal medicineCancerBiologyGeneProteomicsGenetics

Abstract

fetched live from OpenAlex

We thank the Translational Andrology and Urology ( TAU ) editors for providing us this opportunity to reply to the insightful commentary by Conner et al. on our article “Temporal stability and prognostic biomarker potential of the prostate cancer urine transcriptome” (1). Liquid biopsies are one path leading to the future of personalized medicine.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.039
Version: metacan-v3-hybrid-931329e0061cValidation 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: Editorial · Consensus signal: none
Teacher disagreement score0.031
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.039
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0010.001
Science and technology studies0.0030.005
Scholarly communication0.0040.007
Open science0.0020.002
Research integrity0.0310.052
Insufficient payload (model declined to judge)0.0040.004

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 head0.021
GPT teacher head0.292
Teacher spread0.270 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

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

Citations0
Published2020
Admission routes2
Has abstractyes

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