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Record W4403855186 · doi:10.1016/s1470-2045(24)00315-2

Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 2: recommendations for standardisation, validation, and good clinical practice

2024· review· en· W4403855186 on OpenAlexaff
Spyridon Bakas, Philipp Kickingereder, Norbert Galldiks, Thomas C. Booth, Hugo J.W.L. Aerts, Wenya Linda Bi, Benedikt Wiestler, Pallavi Tiwari, Sarthak Pati, Ujjwal Baid, Evan Calabrese, Philipp Lohmann, Martha Nowosielski, Rajan Jain, Rivka R. Colen, Marwa Ismail, Ghulam Rasool, Janine Lupo, Hamed Akbari, J. C. Tonn, David R. Macdonald, Michael A. Vogelbaum, Susan M. Chang, Christos Davatzikos, Javier Villanueva-Meyer, Raymond Y. Huang

Bibliographic record

VenueThe Lancet Oncology · 2024
Typereview
Languageen
FieldMedicine
TopicRadiomics and Machine Learning in Medical Imaging
Canadian institutionsArtificial Intelligence in Medicine (Canada)
FundersDepartment of Radiology, Weill Cornell Medical CollegeDOD Peer Reviewed Cancer Research ProgramNational Institute of Neurological Disorders and StrokeServierNational Cancer InstituteCentre For Medical Engineering, King’s College LondonEuropean Research CouncilDana FoundationEngineering and Physical Sciences Research CouncilPerelman School of Medicine, University of PennsylvaniaNational Institutes of HealthSiemens HealthineersDeutsche ForschungsgemeinschaftNovocureU.S. Department of DefenseEuropean CommissionUniversity of PennsylvaniaMusella Foundation For Brain Tumor Research and InformationStrykerInstituto Tecnológico de Costa RicaUniversity of Wisconsin-MadisonMedical Research CouncilNational Center for Advancing Translational SciencesWellcome TrustU.S. Department of Veterans Affairs
KeywordsMedical physicsClinical PracticeArtificial intelligenceComputer sciencePsychologyEngineeringMedicineFamily medicine

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)0.071
metaresearch head score (Gemma)0.085
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.929
Threshold uncertainty score0.375

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0710.085
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0070.004
Bibliometrics0.0060.004
Science and technology studies0.0010.003
Scholarly communication0.0050.003
Open science0.0050.003
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0030.002

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 head0.264
GPT teacher head0.593
Teacher spread0.329 · 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.

Study designNot applicable
DomainMethods
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

Citations27
Published2024
Admission routes1
Has abstractno

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