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Record W2734587783 · doi:10.3390/cancers9070089

The Role of Radiation Induced Injury on Lung Cancer

2017· article· en· W2734587783 on OpenAlexaff
Stephanie Puukila, Christopher Thome, Antone L. Brooks, Gayle E. Woloschak, Douglas R. Boreham

Bibliographic record

VenueCancers · 2017
Typearticle
Languageen
FieldMedicine
TopicEffects of Radiation Exposure
Canadian institutionsBruce Power (Canada)NOSM UniversityLaurentian University
FundersNational Cancer Institute
KeywordsLung cancerMedicineCancerLungPathologyPneumonitisBeagleFibrosisCancer researchInternal medicine

Abstract

fetched live from OpenAlex

This manuscript evaluates the role of cell killing, tissue disorganization, and tissue damage on the induction of lung cancer following low dose rate radiation exposures from internally deposited radioactive materials. Beagle dogs were exposed by inhalation to 90Y, 91Y, 144Ce, or 90Sr in fused clay particles. Dogs lived out their life span with complete pathology conducted at the time of death. The radiation dose per cell turnover was characterized and related to the cause of death for each animal. Large doses per cell turnover resulted in acute death from lung damage with extensive cell killing, tissue disorganization, chronic inflammatory disease, fibrosis, and pneumonitis. Dogs with lower doses per cell turnover developed a very high frequency of lung cancer. As the dose per cell turnover was further decreased, no marked tissue damage and no significant change in either life span or lung cancer frequency was observed. Radiation induced tissue damage and chronic inflammatory disease results in high cancer frequencies in the lung. At doses where a high frequency of chromosome damage and mutations would be predicted to occur there was no decrease in life span or increase in lung cancer. Such research suggests that cell killing and tissue damage and the physiological responses to that damage are important mechanisms in radiation induced lung cancer.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.768
Threshold uncertainty score0.179

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.009
GPT teacher head0.321
Teacher spread0.312 · 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
GenreEmpirical

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

Citations17
Published2017
Admission routes1
Has abstractyes

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