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Record W4289947652 · doi:10.1080/09553002.2022.2110304

Summary of Radiation Research Society Online 67th Annual Meeting, Symposium on “Radiation and Circulatory Effects”

2022· article· en· W4289947652 on OpenAlexaboutno aff
Helmut Schöllnberger, Lawrence T. Dauer, Richard Wakeford, Julie Constanzo, Ashley P. Golden

Bibliographic record

VenueInternational Journal of Radiation Biology · 2022
Typearticle
Languageen
FieldMedicine
TopicRadiation Dose and Imaging
Canadian institutionsnot available
FundersAgence Nationale de la Recherche
KeywordsEpidemiologyMedicineCohortCohort studyFluoroscopyEnvironmental healthGerontologyInternal medicineSurgery

Abstract

fetched live from OpenAlex

PURPOSE: Annual Meeting, October 3-6 2021. MATERIALS AND METHODS: Different epidemiological cohorts were analyzed with various statistical means common in epidemiology. The cohorts included the one from the U.S. Million Person Study and the Canadian Fluoroscopy Cohort Study. In addition, one of the contributions in our article relies on results from analyses of the Japanese atomic bomb survivors, Russian emergency and recovery workers and cohorts of nuclear workers. The Canadian Fluoroscopy Cohort Study data were analyzed with a larger series of linear and nonlinear dose-response models in addition to the linear no-threshold (LNT) model. RESULTS AND CONCLUSIONS: The talks in this symposium showed that low/moderate acute doses at low/moderate dose rates can be associated with an increased risk of CVD, although some of the epidemiological results for occupational cohorts are equivocal. The usually only limited availability of information on well-known risk factors for circulatory disease (e.g. smoking, obesity, hypertension, diabetes, physical activity) is an important limiting factor that may bias any observed association between radiation exposure and detrimental health outcome, especially at low doses. Additional follow-up and careful dosimetric and outcome assessment are necessary and more epidemiological and experimental research is required. Obtaining reliable information on other risk factors is especially important.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.445
Threshold uncertainty score0.516

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.018
GPT teacher head0.352
Teacher spread0.334 · 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 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

Citations6
Published2022
Admission routes1
Has abstractyes

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