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Record W3159964039 · doi:10.1002/cncr.33618

Ultra‐rare sarcomas: A consensus paper from the Connective Tissue Oncology Society community of experts on the incidence threshold and the list of entities

2021· article· en· W3159964039 on OpenAlexaff
Silvia Stacchiotti, Anna Maria Frezza, Jean‐Yves Blay, Elizabeth H. Baldini, Sylvie Bonvalot, Judith V.M.G. Bovée, Dario Callegaro, Paolo G. Casali, RuRu Chun-Ju Chiang, George D. Demetri, Elisabeth G. Demicco, Jayesh Desai, Mikael Eriksson, Hans Gelderblom, Suzanne George, Mrinal M. Gounder, Alessandro Gronchi, Abha A. Gupta, Rick L. Haas, Andrea Hayes‐Jardon, Peter Hohenberger, Kevin B. Jones, Robin L. Jones, Bernd Kasper, Akira Kawai, David G. Kirsch, Eugene S. Kleinerman, Axel Le Cesne, Jiwon Lim, María Dolores Chirlaque López, Roberta Maestro, Rafael Marcos‐Gragera, Javier Martín‐Broto, Tomohiro Matsuda, Olivier Mir, Shreyaskumar Patel, Chandrajit P. Raut, Albiruni R. Abdul Razak, Damon R. Reed, Piotr Rutkowski, Roberta Sanfilippo, Marta Sbaraglia, Inga‐Marie Schaefer, D. Strauß, Kirsten Sundby Hall, William D. Tap, David M. Thomas, Winette T.A. van der Graaf, Winan J. van Houdt, Otto Visser, Margaret von Mehren, Andrew J. Wagner, Breelyn A. Wilky, Young‐Joo Won, Christopher D.�M. Fletcher, Angelo Paolo Dei Tos, Annalisa Trama

Bibliographic record

VenueCancer · 2021
Typearticle
Languageen
FieldMedicine
TopicSarcoma Diagnosis and Treatment
Canadian institutionsPrincess Margaret Cancer CentreHospital for Sick ChildrenUniversity of TorontoMount Sinai Hospital
FundersEuropean Network for Rare Adult Solid CancersLabEx DEvweCANNational Cancer InstituteNational Institutes of HealthInstitut National Du CancerDirection Générale de l’offre de SoinsInstitut National de la Santé et de la Recherche MédicaleAgence Nationale de la Recherche
KeywordsMedicineIncidence (geometry)SarcomaRare diseaseBone SarcomaCancerPathologyDiseaseInternal medicine

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

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)0.101
metaresearch head score (Gemma)0.126
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.101
Threshold uncertainty score0.535

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1010.126
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.005
Bibliometrics0.0100.007
Science and technology studies0.0040.004
Scholarly communication0.0070.009
Open science0.0080.011
Research integrity0.0130.019
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 head0.048
GPT teacher head0.328
Teacher spread0.280 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations227
Published2021
Admission routes1
Has abstractyes

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