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Record W2373606074 · doi:10.1016/j.ejca.2016.03.081

RECIST 1.1—Update and clarification: From the RECIST committee

2016· article· en· W2373606074 on OpenAlexafffund
Lawrence H. Schwartz, Saskia Litière, Elisabeth G.E. de Vries, 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, Yan Liu, Patrick Therasse, Jedd D. Wolchok, Lesley Seymour

Bibliographic record

VenueEuropean Journal of Cancer · 2016
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Treatments and Mutations
Canadian institutionsQueen's University
FundersCanadian Cancer Society Research InstituteNational Cancer InstituteNational Cancer Research Institute
KeywordsResponse Evaluation Criteria in Solid TumorsMedicineMedical physicsRadiologyDiseaseProgressive diseaseInternal 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)0.098
metaresearch head score (Gemma)0.235
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: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.902
Threshold uncertainty score0.519

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0980.235
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0090.007
Bibliometrics0.0080.008
Science and technology studies0.0020.003
Scholarly communication0.0110.008
Open science0.0150.006
Research integrity0.0250.039
Insufficient payload (model declined to judge)0.0070.016

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 head0.022
GPT teacher head0.317
Teacher spread0.295 · 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
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

Citations1,894
Published2016
Admission routes2
Has abstractno

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