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Record W2884824167 · doi:10.1002/path.5139

Lynch syndrome – cancer pathways, heterogeneity and immune escape

2018· review· en· W2884824167 on OpenAlexaff
Sidhant Seth, Ann Ager, Mark J. Arends, Ian M. Frayling

Bibliographic record

VenueThe Journal of Pathology · 2018
Typereview
Languageen
FieldMedicine
TopicGenetic factors in colorectal cancer
Canadian institutionsInstitute of Infection and Immunity
FundersMedical Research CouncilCancer Research UK
KeywordsMicrosatellite instabilityLynch syndromeColorectal cancerCancerDNA mismatch repairPathologicalBiologyOncologyMedicineCancer researchBioinformaticsMicrosatelliteGeneInternal medicineGeneticsAllele

Abstract

fetched live from OpenAlex

Recent work has provided evidence for genetic and molecular heterogeneity in colorectal cancers (CRCs) arising in patients with Lynch syndrome (LS), dividing these into two groups: G1 and G2. In terms of mutation and gene expression profile, G1 CRCs bear resemblance to sporadic CRCs with microsatellite instability (MSI), whereas G2 CRCs are more similar to microsatellite-stable CRCs. Here we review the current state of knowledge on pathways of precursor progression to CRC in LS and how these might tie in with the new findings. Immunotherapies are an active field of research for MSI cancers and their potential use for cancer therapy for both sporadic and LS MSI cancers is discussed. Copyright © 2018 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.978
Threshold uncertainty score0.816

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.069
GPT teacher head0.360
Teacher spread0.291 · 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 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

Citations43
Published2018
Admission routes1
Has abstractyes

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