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Record W2252848846 · doi:10.1101/pdb.top069948

Methods to Study Metastasis in Genetically Modified Mice

2016· article· en· W2252848846 on OpenAlexafffund
Farhia Kabeer, Levi J. Beverly, Guillaume Darrasse-Jèze, Katrina Podsypanina

Bibliographic record

VenueCold Spring Harbor Protocols · 2016
Typearticle
Languageen
FieldMedicine
TopicCancer Cells and Metastasis
Canadian institutionsMcGill UniversityUniversité de MontréalMontreal Clinical Research Institute
FundersInstitut de Recherche Clinique De Montréal
KeywordsXenotransplantationMetastasisTransgeneGenetically modified organismGenetically modified mouseBiologyCancerCancer researchMammary tumorGene knockoutGeneComputational biologyBreast cancerMedicineTransplantationGeneticsInternal medicine

Abstract

fetched live from OpenAlex

Metastasis is often modeled by xenotransplantation of cell lines in immunodeficient mice. A wealth of information about tumor cell behavior in the new environment is obtained from these efforts. Yet by design, this approach is "tumor-centric," as it focuses on cell-autonomous determinants of human tumor dissemination in mouse tissues, in effect using the animal body as a sophisticated "Petri dish" providing nutrients and support for tumor growth. Transgenic or gene knockout mouse models of cancer allow the study of tumor spread as a systemic disease and offer a complimentary approach for studying the natural history of cancer. This introduction is aimed at describing the overall methodological approach to studying metastasis in genetically modified mice, with a particular focus on using animals with regulated expression of potent human oncogenes in the breast.

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)0.003
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: Methods · Consensus signal: Methods
Teacher disagreement score0.015
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0030.002
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0150.007

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 head0.067
GPT teacher head0.400
Teacher spread0.333 · 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
GenreMethods

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

Citations12
Published2016
Admission routes2
Has abstractyes

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