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Record W2943698134 · doi:10.1093/rpd/ncz068

DOMESTIC RADON EXPOSURE AND CHILDHOOD LEUKAEMIA AND LYMPHOMA: A POPULATION-BASED STUDY IN CANADA

2019· article· en· W2943698134 on OpenAlexaffabout
Jing Chen, Lin Xie

Bibliographic record

VenueRadiation Protection Dosimetry · 2019
Typearticle
Languageen
FieldHealth Professions
TopicRadioactivity and Radon Measurements
Canadian institutionsPublic Health Agency of CanadaHealth Canada
Fundersnot available
KeywordsChildhood leukaemiaRadonRadon exposureEnvironmental healthMedicinePopulationPediatricsNuclear physicsPhysics

Abstract

fetched live from OpenAlex

In this paper, we revisit the possibility, first raised using a data set collected in the 1970s, that there is a link between average radon concentrations and the incidence of childhood leukaemia and lymphoma in Canada. Following the launch of the National Radon Program in 2007, Health Canada completed a long-term radon survey in 33 census metropolitan areas (CMAs), which covers about 70% of the Canadian population. We used this data, together with leukaemia and lymphoma incidence rates among children (0-14 years of age) in the past decade (2006-15), and tried to link the city-level average radon concentrations to the leukaemia and lymphoma incidence rates in 33 major Canadian cities. Analyses were conducted for six subtypes (ALL, AML, CMD, HL, NHL and BL) of leukaemia and lymphoma. Estimated doses to red bone marrow from domestic radon exposure were low and we did not find any association between radon exposure at home and the increased risk for developing leukaemia among children under 15 years of age living in the CMAs. The results indicate a slight positive association for AML among 1-4 year males in CMAs of Peer Group C and NHL among 5-9 year females in CMAs of Peer Group A; however, these should be interpreted with caution owing to the crude exposure assessment and possibilities of other confounding factors.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.017
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.005
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.294
Teacher spread0.278 · 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

Citations11
Published2019
Admission routes2
Has abstractyes

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