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Record W3170601740 · doi:10.1016/j.ejca.2021.04.041

Integration of quantitative imaging biomarkers in clinical trials for MR-guided radiotherapy: Conceptual guidance for multicentre studies from the MR-Linac Consortium Imaging Biomarker Working Group

2021· article· en· W3170601740 on OpenAlexaff
Petra J. van Houdt, Hina Saeed, Daniela Thorwarth, Clifton D. Fuller, William A. Hall, Brigid A. McDonald, Amita Shukla‐Dave, Ernst S. Kooreman, M.E.P. Philippens, Astrid L.H.M.W. van Lier, Rick Keesman, Faisal Mahmood, Catherine Coolens, Teodor Stanescu, Jihong Wang, Neelam Tyagi, Andreas Wetscherek, Uulke A. van der Heide

Bibliographic record

VenueEuropean Journal of Cancer · 2021
Typearticle
Languageen
FieldMedicine
TopicRadiomics and Machine Learning in Medical Imaging
Canadian institutionsUniversity of TorontoPrincess Margaret Cancer CentreUniversity Health Network
FundersNational Institute of Dental and Craniofacial ResearchNational Institute of Biomedical Imaging and BioengineeringNational Cancer InstituteCancer Research UK
KeywordsMedical physicsClinical trialQuality assuranceMedicineImaging biomarkerMagnetic resonance imagingComputer scienceRadiologyPathology

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)0.384
metaresearch head score (Gemma)0.314
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: Methods
Teacher disagreement score0.616
Threshold uncertainty score0.759

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3840.314
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0100.008
Bibliometrics0.0050.006
Science and technology studies0.0010.005
Scholarly communication0.0200.010
Open science0.0070.010
Research integrity0.0070.009
Insufficient payload (model declined to judge)0.0050.002

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 head0.206
GPT teacher head0.483
Teacher spread0.277 · 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
Published2021
Admission routes1
Has abstractno

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