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Record W2252848846 · doi:10.1101/pdb.top069948

Methods to Study Metastasis in Genetically Modified Mice

2016· article· en· W2252848846 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

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.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.116
Threshold uncertainty score0.678

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.067
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