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Record W2532595713 · doi:10.1101/cshperspect.a028373

The Role of Aneuploidy in Cancer Evolution

2016· review· en· W2532595713 on OpenAlexfundno aff
Laurent Sansregret, Charles Swanton

Bibliographic record

VenueCold Spring Harbor Perspectives in Medicine · 2016
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicChromosomal and Genetic Variations
Canadian institutionsnot available
FundersStand Up To CancerCancer Research UKNovo Nordisk FondenUniversity College LondonNational Institute for Health and Care ResearchNovo NordiskCanadian Institutes of Health ResearchRoyal SocietyFrancis Crick InstituteEuropean Molecular Biology OrganizationProstate Cancer FoundationCRUK Lung Cancer Centre of ExcellenceBreast Cancer Research Foundation
KeywordsKingdomCancerExcellenceLung cancerGenealogyMedicineBiologyHistoryGeneticsLawPolitical scienceInternal medicine

Abstract

fetched live from OpenAlex

Chromosomal aberrations during cell division represent one of the first recognized features of human cancer cells, and modern detection methods have revealed the pervasiveness of aneuploidy in cancer. The ongoing karyotypic changes brought about by chromosomal instability (CIN) contribute to tumor heterogeneity, drug resistance, and treatment failure. Whole-chromosome and segmental aneuploidies resulting from CIN have been proposed to allow "macroevolutionary" leaps that may contribute to profound phenotypic change. In this review, we will outline evidence indicating that aneuploidy and CIN contribute to cancer evolution.

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)0.001
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: Review
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.029
GPT teacher head0.299
Teacher spread0.270 · 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

Citations275
Published2016
Admission routes1
Has abstractyes

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