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Record W2802206159 · doi:10.1016/s0140-6736(18)30789-x

International validation of the consensus Immunoscore for the classification of colon cancer: a prognostic and accuracy study

2018· article· en· W2802206159 on OpenAlexaff
Franck Pagès, Bernhard Mlecnik, Florence Marliot, Gabriela Bindea, Fang‐Shu Ou, Carlo Bifulco, Alessandro Lugli, Inti Zlobec, Tilman T. Rau, Martin D. Berger, Irıs D. Nagtegaal, Elisa Vink‐Börger, Arndt Hartmann, Carol Geppert, Julie Kolwelter, Susanne Merkel, Robert Grützmann, Marc Van den Eynde, Anne Jouret‐Mourin, Alex Kartheuser, Daniel Léonard, Christophe Remue, Julia Y. Wang, Prashant Bavi, Michael H. A. Roehrl, Pamela S. Ohashi, Linh T. Nguyen, SeongJun Han, Heather MacGregor, Sara Hafezi‐Bakhtiari, Bradly G. Wouters, Giuseppe Masucci, Emilia Andersson, Eva Závadová, Michal Vočka, Jiří Špaček, Luboš Petruželka, B Konopásek, Pavel Dundr, Helena Skálová, Kristýna Němejcová, Gerardo Botti, Fabiana Tatangelo, Paolo Delrio, Gennaro Ciliberto, Michele Maio, Luigi Laghi, Fabio Grizzi, Tessa Fredriksen, Bénédicte Buttard, Mihaela Angelova, Angela Vasaturo, Pauline Maby, S. Church, Helen K. Angell, Lucie Lafontaine, Daniela Bruni, Carine El Sissy, Nacilla Haicheur, Amos Kirilovsky, Anne Berger, Christine Lagorce, Jeffrey P. Meyers, Christopher Paustian, Zipei Feng, Carmen Ballesteros‐Merino, Jeroen R. Dijkstra, Carlijn van de Water, Shannon van Vliet, Nikki Knijn, Ana-Maria Mușină, Dragoş Viorel Scripcariu, Boryana Konstantinova Popivanova, Mingli Xu, Tomonobu Fujita, Shoichi Hazama, Nobuaki Suzuki, Hiroaki Nagano, Kiyotaka Okuno, Toshihiko Torigoe, Noriyuki Sato, Tomohisa Furuhata, Ichiro Takemasa, Kyogo Itoh, Prabhudas S. Patel, Hemangini H. Vora, Birva Shah, Jayendrakumar B. Patel, Kruti N. Rajvik, Shashank Pandya, Shilin N. Shukla, Yili Wang, Guanjun Zhang, Yutaka Kawakami, Francesco M. Marincola, Paolo A. Ascierto, Daniel J. Sargent, Bernard A. Fox, Jérôme Galon

Bibliographic record

VenueThe Lancet · 2018
Typearticle
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsUniversity of TorontoUniversity Health Network
FundersInstitut National de la Santé et de la Recherche MédicaleNational Institutes of HealthAgence Nationale de la Recherche
KeywordsMedicineColorectal cancerOncologyInternal medicineCancerNomogramConfoundingProportional hazards modelStage (stratigraphy)ImmunotherapyClinical endpointPrognostic variableClinical trial

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)0.030
metaresearch head score (Gemma)0.053
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.157

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0300.053
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0020.002
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 head0.078
GPT teacher head0.370
Teacher spread0.292 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations2,058
Published2018
Admission routes1
Has abstractno

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