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Record W4224249539 · doi:10.1038/s41591-022-01775-6

Reimagining patient-centric cancer clinical trials: a multi-stakeholder international coalition

2022· article· en· W4224249539 on OpenAlexafffund
Bob T. Li, Bobby Daly, Mary Gospodarowicz, Monica M. Bertagnolli, Otis W. Brawley, Bruce A. Chabner, Lola A. Fashoyin‐Aje, R. Angelo de Claro, Elizabeth Franklin, Jennifer S. Mills, Jeff Legos, Karen Kaucic, Mark Junjie Li, Lydia Thé, Tina Hou, Tinghui Wu, Björn Albrecht, Yi Shao, Justin Finnegan, Jing Qian, Javad Shahidi, Eduard Gasal, Craig Tendler, Geoffrey Kim, James Yan, Phuong K. Morrow, Charles S. Fuchs, Lianshan Zhang, Robert LaCaze, Stefan Oelrich, Martin J. Murphy, Richard Pazdur, Kevin Rudd, Yi‐Long Wu

Bibliographic record

VenueNature Medicine · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
FundersFoundation MedicinePharmacyclicsJohns Hopkins Bloomberg School of Public HealthWeill Cornell Medical CollegeGenentechShanghai Jiao Tong UniversityAstellas PharmaEisaiBayer HealthCareGovernment of OntarioOntario Institute for Cancer ResearchHSBC Bank USABaxaltaVarian Medical SystemsJohns Hopkins UniversityMemorial Sloan-Kettering Cancer CenterDaiichi Sankyo EuropeNational Cancer InstituteBayerGilead SciencesUniversity of TorontoYale UniversityExelixisGuangdong Provincial People's HospitalLeidosBreast Cancer Research FoundationSanofiAstraZenecaEli Lilly and CompanyQueen's UniversityBloomberg L.P.CelgeneYale Cancer CenterInnovent BiologicsIncytePfizerNational Institutes of HealthNateraRobert Wood Johnson FoundationAmgenBeiGeneAlliance for Clinical Trials in Oncology FoundationJiangsu Hengrui MedicinePrevent Cancer FoundationTeva Pharmaceutical IndustriesMassachusetts General Hospital
KeywordsLeverage (statistics)Government (linguistics)Clinical trialStakeholderPandemicPublic relationsPolitical scienceBusinessCoronavirus disease 2019 (COVID-19)Public administrationMedicine

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.472
metaresearch head score (Gemma)0.297
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.528
Threshold uncertainty score0.651

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.4720.297
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0030.003
Science and technology studies0.0130.012
Scholarly communication0.0340.015
Open science0.0100.048
Research integrity0.0340.055
Insufficient payload (model declined to judge)0.0100.001

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 head0.119
GPT teacher head0.433
Teacher spread0.315 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designTheoretical or conceptual
DomainMethods
GenreMethods

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

Citations52
Published2022
Admission routes2
Has abstractno

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