MétaCan
Menu
Back to cohort
Record W3009345706 · doi:10.1002/path.5422

Molecular pathology of Lynch syndrome

2020· review· en· W3009345706 on OpenAlexaff
Guia Cerretelli, Ann Ager, Mark J. Arends, Ian M. Frayling

Bibliographic record

VenueThe Journal of Pathology · 2020
Typereview
Languageen
FieldMedicine
TopicGenetic factors in colorectal cancer
Canadian institutionsInstitute of Infection and Immunity
FundersBiotechnology and Biological Sciences Research CouncilMedical Research CouncilCancer Research UK
KeywordsLynch syndromeMolecular pathologyPathologyMedicineBiologyInternal medicineGeneticsCancerDNA mismatch repairColorectal cancerGene

Abstract

fetched live from OpenAlex

Lynch syndrome (LS) is characterised by predisposition to colorectal, endometrial, and other cancers and is caused by inherited pathogenic variants affecting the DNA mismatch repair (MMR) genes MLH1, MSH2, MSH6, and PMS2. It is probably the most common predisposition to cancer, having an estimated prevalence of between 1/100 and 1/180. Resources such as the International Society for Gastrointestinal Hereditary Cancer's MMR gene variant database, the Prospective Lynch Syndrome Database (PLSD), and the Colon Cancer Family Register (CCFR), as well as pathological and immunological studies, are enabling advances in the understanding of LS. These include defined criteria by which to interpret gene variants, the function of MMR in the normal control of apoptosis, definition of the risks of the various cancers, and the mechanisms and pathways by which the colorectal and endometrial tumours develop, including the critical role of the immune system. Colorectal cancers in LS can develop along three pathways, including flat intramucosal lesions, which depend on the underlying affected MMR gene. This gives insights into the limitations of colonoscopic surveillance and highlights the need for other forms of anti-cancer prophylaxis in LS. Finally, it shows that the processes of autoimmunisation and immunoediting fundamentally constrain the development of tumours in LS and explain the efficacy of immune checkpoint blockade therapy in MMR-deficient tumours. © 2020 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 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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.041
GPT teacher head0.345
Teacher spread0.304 · 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 designNot applicable
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

Citations174
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of PathologySame topicGenetic factors in colorectal cancerFrench-language works237,207