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Physical Activity and Cancer Prevention: Animal-Tumor Models

2003· review· en· W2033280762 on OpenAlexaff
Laurie Hoffman‐Goetz

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2003
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Research and Treatments
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsCancerCarcinogenesisAnimal modelAnimal studiesMedicineCancer preventionCarcinogenCancer researchBiologyInternal medicineGenetics

Abstract

fetched live from OpenAlex

PURPOSE: The aims of this paper are to briefly review the types of animal and tumor models used in carcinogenesis studies and to consider their utility in studies of physical activity and cancer. METHODS: Published data from animal studies using tumor models for which corresponding human epidemiologic evidence shows a clear association between physical activity and that cancer (i.e., colon and breast) are reviewed. RESULTS: A variety of animal-tumor models have been used in cancer studies including spontaneous tumors, chemically induced tumors, orthotopic and syngeneic tumor transplantation, injected tumors, and genetically engineered (transgenic, knockout, and mutation-induced) mice with a predisposition to neoplasia. The most commonly used animal-tumor model in the study of physical activity has been the chemical carcinogenesis model. Methodological limitations of the various animal-tumor models are described including variations in dosing, route of administration, and type of carcinogen used, and forced versus voluntary exercise effects. CONCLUSIONS: Animal-tumor models are useful for understanding specific aspects of the carcinogenesis process and the interaction of this process with exercise. There is no one animal-tumor model that is ideally suited for studying physical activity interventions. However, animal-tumor models can be viewed as complementary to epidemiologic studies and human clinical trials in the area of physical activity and cancer prevention.

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)0.000
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 score0.997
Threshold uncertainty score0.930

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.042
GPT teacher head0.392
Teacher spread0.350 · 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

Citations40
Published2003
Admission routes1
Has abstractyes

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