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Record W2397768676 · doi:10.1016/j.ejca.2016.03.082

RECIST 1.1 – Standardisation and disease-specific adaptations: Perspectives from the RECIST Working Group

2016· review· en· W2397768676 on OpenAlexafffund
Lawrence H. Schwartz, Lesley Seymour, Saskia Litière, Robert Ford, Stephen J. Gwyther, Sumithra J. Mandrekar, Lalitha Shankar, Jan Bogaerts, Alice P. Chen, Janet Dancey, Wendy Hayes, F. Stephen Hodi, Otto S. Hoekstra, Erich P. Huang, Nancy U. Lin, Patrick Therasse, Jedd D. Wolchok, Elisabeth G.E. de Vries

Bibliographic record

VenueEuropean Journal of Cancer · 2016
Typereview
Languageen
FieldMedicine
TopicRadiomics and Machine Learning in Medical Imaging
Canadian institutionsQueen's University
FundersNational Cancer InstituteCanadian Cancer Society Research Institute
KeywordsResponse Evaluation Criteria in Solid TumorsMedicineClinical trialMedical physicsClinical endpointBiomarkerImaging biomarkerRadiologyPhases of clinical researchInternal medicineMagnetic resonance imaging

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)0.014
metaresearch head score (Gemma)0.018
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 score0.986
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.018
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0020.003
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0030.001
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0020.001

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 head0.059
GPT teacher head0.346
Teacher spread0.288 · 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

Citations380
Published2016
Admission routes2
Has abstractno

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