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Record W2936672856 · doi:10.1097/hp.0000000000001039

Effect of Radiation Dose Rate on Cancer Mortality among Nuclear Workers: Reanalysis of Hanford Data

2019· article· en· W2936672856 on OpenAlexaboutno aff
Michiya Sasaki, Shinichi Kudo, Hiroshige Furuta

Bibliographic record

VenueHealth Physics · 2019
Typearticle
Languageen
FieldMedicine
TopicRadiation Dose and Imaging
Canadian institutionsnot available
Fundersnot available
KeywordsHanford SiteMedicineRelative riskNuclear medicinePopulationConfidence intervalEnvironmental healthDemographyToxicologyRadioactive wasteWaste managementInternal medicineBiology

Abstract

fetched live from OpenAlex

The excess relative risk of mortality for all cancers excluding leukemia among nuclear workers was reanalyzed by taking the annual dose as the dose rate into consideration using publicly available epidemiological data from the Hanford site dedicated to the cohort study of nuclear workers in the United States, the United Kingdom, and Canada (Three Countries Study). Values of the dose rate (cut points) were chosen at 2 mSv y intervals from 2 to 40 mSv y, and risk estimates were made for 32,988 workers, considering doses accumulated below the cut point and above the cut point to have different effects. Although the procedure to extract the study population and the methodology used for analysis basically followed those in the Three Countries Study, additional examinations were also carried out for different risk models, lag periods, and impacts of adjusting the monitoring period to find the effect of the dose rate. As a result, no statistically significant difference in dose rate was found among the excess relative risks under different calculation conditions.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.101
Threshold uncertainty score0.200

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.398
Teacher spread0.356 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations3
Published2019
Admission routes1
Has abstractyes

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