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Record W2996503392 · doi:10.1177/1559325819893195

A Nontarget Mechanism to Explain Carcinogenesis Following α-Irradiation

2019· article· en· W2996503392 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.

Bibliographic record

VenueDose-Response · 2019
Typearticle
Languageen
FieldMedicine
TopicEffects of Radiation Exposure
Canadian institutionsCanadian Nuclear Laboratories
Fundersnot available
KeywordsCarcinogenesisInflammationCancer researchFibrosisProgrammed cell deathCancerIrradiationMedicineChemistryPathologyImmunologyInternal medicineBiochemistryApoptosisPhysics

Abstract

fetched live from OpenAlex

This commentary highlights the published data on the metabolic processes that lead to the development of cancer following intakes of asbestos and chemical agents. Following exposure to both, the key initiating event is cell injury leading to cell death that may further lead to inflammation, fibrosis, and cancer. Since α-particle transits also kill cells, it is suggested that cell death and inflammation will also trigger carcinogenesis within tissues irradiated by these particles. Such an explanation would be consistent with the inflammation and fibrosis seen in tumor-bearing tissues irradiated by radon-222, radium-226, thorium-232, plutonium-239, and other α-emitting radionuclides. It would also provide an explanation for dose-related changes in latency and in the similar dose-responses for the same tissue in differently sized species.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.852
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.002

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 head0.008
GPT teacher head0.263
Teacher spread0.256 · 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