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Record W4410382902 · doi:10.1136/gutjnl-2025-335091

Large language models for detecting colorectal polyps in endoscopic images

2025· article· en· W4410382902 on OpenAlexaff
Davide Massimi, Yuichi Mori, Giulio Antonelli, Tommy Rizkala, Marco Spadaccini, Chiara Lena, Sravanthi Parasa, Raf Bisschops, Daniel von Renteln, Susanne O'Reilly, Prateek Sharma, Douglas K. Rex, Michael Bretthauer, Alessandro Repici, Elena Demomi, Cesare Hassan

Bibliographic record

VenueGut · 2025
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsUniversité de Montréal
FundersNorges ForskningsrådEuropean Commission
KeywordsColorectal PolypMedicineComputer scienceColonoscopyRectal PolypRadiologyArtificial intelligenceNatural language processingColorectal cancerRectumInternal medicineCancer

Abstract

fetched live from OpenAlex

sponsorship: Cesare Hassan and Alessandro Repici are supported by the European Commission (Horizon Europe 101057099). The Associazione Italiana per la Ricerca sul Cancro (AIRC): IG 2022 - ID. 27843 project / (AIRC) IG 2023 - ID. 29220 project. And Bando PNRR- MCNT2- 2023- 12377041. Yuichi Mori is supported by the European Commission (Horizon Europe: 101057099). Luca Carlini is supported by the National Plan for NRRP Complementary Investments (PNC, established with the decree- law 6 May 2021, n. 59, converted by law n. 101 of 2021) in the call for the funding of research initiatives for technologies and innovative trajectories in the health and care sectors (Directorial Decree n. 931 of 06- 06- 2022) - project n. PNC0000003 - AdvaNced Technologies for Human- centrEd Medicine (project acronym: ANTHEM). This work reflects only the authors' views and opinions; neither the Ministry for University and Research nor the European Commission can be considered responsible for them. Michael Bretthauer is supported by the European Commission (Horizon Europe No. 101057099), Norwegian National Clinical Trial Mechanism (grant 36935), and Norwegian Research Council (grant 315410). Raf Bisschops is supported by a grant of research foundation Flanders (G072621N) and FKO of KU Leuven. Chiara Lena is supported by Multilayered Urban Sustainability Action (MUSA) project (ECS00000037), funded by the European Union - NextGenerationEU, under the National Recovery and Resilience Plan (NRRP) (European Commission|101057099, The Associazione Italiana per la Ricerca sul Cancro (AIRC)|27843, The Associazione Italiana per la Ricerca sul Cancro (AIRC)|29220, The Associazione Italiana per la Ricerca sul Cancro (AIRC)|12377041, National Plan for NRRP Complementary Investments|59, National Plan for NRRP Complementary Investments|101, National Plan for NRRP Complementary Investments|931, National Plan for NRRP Complementary Investments|PNC0000003, Norwegian National Clinical Trial Mechanism|36935, Norwegian Research Council|315410, Research foundation Flanders|G072621N, FKO of KU Leuven, Multilayered Urban Sustainability Action (MUSA) project -European Union - NextGenerationEU, under the National Recovery and Resilience Plan (NRRP)|ECS00000037)

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.343
Threshold uncertainty score0.318

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.013
GPT teacher head0.301
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations8
Published2025
Admission routes1
Has abstractyes

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