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Record W4281703599 · doi:10.1038/s41416-022-01859-7

Histopathological growth patterns of liver metastasis: updated consensus guidelines for pattern scoring, perspectives and recent mechanistic insights

2022· review· en· W4281703599 on OpenAlexafffund
Emily Latacz, Diederik J. Höppener, Ali Bohlok, Sophia Leduc, Sébastien Tabariès, Carlos Fernández Moro, Claire Lugassy, Hanna Nyström, Béla Bozóky, Giuseppe Floris, Natalie Geyer, Pnina Brodt, Laura Lladó, Laura Van Mileghem, Maxim De Schepper, Ali W. Majeed, Anthoula Lazaris, Piet Dirix, Qianni Zhang, Stephanie Petrillo, Sophie Vankerckhove, Ines Joye, Yannick Meyer, Alexander Gregorieff, Nuria Ruiz Roig, Fernando Vidal‐Vanaclocha, Denis Larsimont, Rui Caetano Oliveira, Peter Metrakos, Dirk J. Grünhagen, Irıs D. Nagtegaal, David G. Mollevı́, William R. Jarnagin, Michael I. D’Angelica, Andrew R. Reynolds, Michail Doukas, Christine Desmedt, Luc Dirix, Vincent Donckier, Peter M. Siegel, Raymond L. Barnhill, Marco Gerling, Cornelis Verhoef, Peter Vermeulen

Bibliographic record

VenueBritish Journal of Cancer · 2022
Typereview
Languageen
FieldMedicine
TopicRadiomics and Machine Learning in Medical Imaging
Canadian institutionsMcGill University Health CentreMcGill University
FundersNational Cancer InstituteEuropean Regional Development FundEngineering and Physical Sciences Research CouncilAgència de Gestió d'Ajuts Universitaris i de RecercaCanadian Institutes of Health ResearchFondation contre le CancerStichting Tegen KankerGeneralitat de CatalunyaUmeå UniversitetKoning BoudewijnstichtingKnut och Alice Wallenbergs StiftelseCancerfondenVetenskapsrådetGovernment of CanadaCancer Research Foundation in Northern SwedenResearch Councils UKMcGill University
KeywordsHistopathologyMetastasisLiver cancerBiologyCancerColorectal cancerPathologyMedicineBioinformaticsInternal 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)0.006
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0050.004
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0030.001
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 head0.104
GPT teacher head0.396
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 designSystematic review
Domainnot available
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

Citations89
Published2022
Admission routes2
Has abstractno

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