MétaCan
Menu
Back to cohort
Record W4387965670 · doi:10.1038/s41389-023-00492-0

Molecular mechanisms in colitis-associated colorectal cancer

2023· review· en· W4387965670 on OpenAlexfundno aff
Royce Zhou, Noam Harpaz, Steven H. Itzkowitz, Ramon Parsons

Bibliographic record

VenueOncogenesis · 2023
Typereview
Languageen
FieldMedicine
TopicGenetic factors in colorectal cancer
Canadian institutionsnot available
FundersNational Cancer InstituteU.S. Department of Health and Human ServicesAbbVie CanadaNational Institutes of HealthRegeneron PharmaceuticalsEli Lilly and Company
KeywordsField cancerizationEpigeneticsCarcinogenesisColorectal cancerInflammatory bowel diseaseBiologyPathogenesisMouse model of colorectal and intestinal cancerCancer researchContext (archaeology)CancerDiseaseKRASBioinformaticsMedicineImmunologyGeneticsPathologyGene

Abstract

fetched live from OpenAlex

Sustained chronic inflammation of the large intestine leads to tissue damage and repair, which is associated with an increased incidence of colitis-associated colorectal cancer (CAC). The genetic makeup of CAC is somewhat similar to sporadic colorectal carcinoma (sCRC), but there are differences in the sequence and timing of alterations in the carcinogenesis process. Several models have been developed to explain the development of CAC, particularly the "field cancerization" model, which proposes that chronic inflammation accelerates mutagenesis and selects for the clonal expansion of phenotypically normal, pro-tumorigenic cells. In contrast, the "Big Bang" model posits that tumorigenic clones with multiple driver gene mutations emerge spontaneously. The details of CAC tumorigenesis-and how they differ from sCRC-are not yet fully understood. In this Review, we discuss recent genetic, epigenetic, and environmental findings related to CAC pathogenesis in the past five years, with a focus on unbiased, high-resolution genetic profiling of non-dysplastic field cancerization in the context of inflammatory bowel disease (IBD).

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.974
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.062
GPT teacher head0.371
Teacher spread0.309 · 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.

Study designOther design
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
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueOncogenesisSame topicGenetic factors in colorectal cancerFrench-language works237,207