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Record W4282942659 · doi:10.1038/s41746-022-00613-w

Prostate cancer therapy personalization via multi-modal deep learning on randomized phase III clinical trials

2022· article· en· W4282942659 on OpenAlexaff
Andre Esteva, Jean Feng, Douwe van der Wal, Shih-Cheng Huang, Jeffry Simko, Sandy DeVries, Emmalyn Chen, Edward M. Schaeffer, Todd M. Morgan, Yilun Sun, Amirata Ghorbani, Nikhil Naik, Dhruv Nathawani, Richard Socher, Jeff M. Michalski, Mack Roach, Thomas M. Pisansky, Jedidiah M. Monson, Farah Naz, James A. Wallace, Michelle Ferguson, Jean-Paul Bahary, James Zou, Matthew P. Lungren, Serena Yeung, Ashley E. Ross, Michael Jonathan Kucharczyk, Luís Souhami, Leslie Ballas, Christopher A. Peters, Sandy Liu, Alexander G. Balogh, Pamela Randolph-Jackson, David L. Schwartz, M.R. Girvigian, Naoyuki G. Saito, Adam Raben, Rachel Rabinovitch, Khalil Katato, Howard M. Sandler, Phuoc T. Tran, Daniel E. Spratt, Stephanie L. Pugh, Felix Y. Feng, Osama Mohamad

Bibliographic record

Venuenpj Digital Medicine · 2022
Typearticle
Languageen
FieldMedicine
TopicRadiomics and Machine Learning in Medical Imaging
Canadian institutionsMcGill University Health CentreNova Scotia Cancer CentreCentre Hospitalier de l’Université de MontréalHorizon Health NetworkOccupational Cancer Research CentreSaint John Regional Hospital
FundersNational Cancer InstituteNRG Oncology
KeywordsRandomized controlled trialModalProstate cancerPersonalizationClinical trialMedicineCancerMedical physicsComputer scienceArtificial intelligenceOncologyInternal medicineWorld Wide Web

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

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)0.048
metaresearch head score (Gemma)0.056
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.252

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0480.056
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.073
GPT teacher head0.451
Teacher spread0.378 · 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 designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations197
Published2022
Admission routes1
Has abstractyes

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