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Record W2910881983 · doi:10.1016/j.yrtph.2019.01.021

Chemical carcinogenicity revisited 1: A unified theory of carcinogenicity based on contemporary knowledge

2019· review· en· W2910881983 on OpenAlexfundno aff
Douglas C. Wolf, Samuel M. Cohen, Alan R. Boobis, Vicki L. Dellarco, Penelope A. Fenner-Crisp, Angelo Moretto, Timothy P. Pastoor, Rita Schoeny, Jennifer Seed, John E. Doe

Bibliographic record

VenueRegulatory Toxicology and Pharmacology · 2019
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCarcinogens and Genotoxicity Assessment
Canadian institutionsnot available
FundersHealth CanadaU.S. Food and Drug AdministrationNational Institutes of HealthSwiss Centre for Applied Human ToxicologyEuropean Food Safety AuthorityWorld Health OrganizationU.S. Environmental Protection Agency
KeywordsCarcinogenCarcinogenesisCell growthCancerCancer cellDNA damageCancer researchBiologyCellDNAToxicologyComputational biologyGenetics

Abstract

fetched live from OpenAlex

Developments in the understanding of the etiology of cancer have profound implications for the way the carcinogenicity of chemicals is addressed. This paper proposes a unified theory of carcinogenesis that will illuminate better ways to evaluate and regulate chemicals. In the last four decades, we have come to understand that for a cell and a group of cells to begin the process of unrestrained growth that is defined as cancer, there must be changes in DNA that reprogram the cell from normal to abnormal. Cancer is the consequence of DNA coding errors that arise either directly from mutagenic events or indirectly from cell proliferation especially if sustained. Chemicals that act via direct interaction with DNA can induce cancer because they cause mutations which can be carried forward in dividing cells. Chemicals that act via non-genotoxic mechanisms must be dosed to maintain a proliferative environment so that the steps toward neoplasia have time to occur. Chemicals that induce increased cellular proliferation can be divided into two categories: those which act by a cellular receptor to induce cellular proliferation, and those which act via non-specific mechanisms such as cytotoxicity. This knowledge has implications for testing chemicals for carcinogenic potential and risk management.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.881
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0020.001
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 head0.059
GPT teacher head0.349
Teacher spread0.290 · 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.

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

Citations74
Published2019
Admission routes1
Has abstractyes

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