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Record W2140193731 · doi:10.1093/aje/kwm020

Ultraviolet Radiation Exposure and Risk of Non-Hodgkin's Lymphoma

2007· article· en· W2140193731 on OpenAlexaff
Y. Zhang, Theodore R. Holford, Brian P. Leaderer, Peter Boyle, Yunxia Zhu, Renheng Wang, K Zou, Bing Zhang, John Pierce Wise, Qiu Qin, Briseis A. Kilfoy, Jiali Han, Tongzhang Zheng

Bibliographic record

VenueAmerican Journal of Epidemiology · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsMcGill University
FundersNational Cancer InstituteState of Connecticut Department of Public Health
KeywordsOdds ratioConfidence intervalMedicineLymphomaHodgkin lymphomaUltraviolet radiationPopulationDermatologyInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

Sun exposure has been suggested to increase the risk of non-Hodgkin's lymphoma. The authors analyzed data from a population-based, case-control study of Connecticut women between 1996 and 2000 to study the hypothesis. Women who reported having had a suntan experienced an increased risk of non-Hodgkin's lymphoma with increasing duration (p(trend) = 0.0062) compared with women who reported never having had a suntan. An almost threefold increased risk of non-Hodgkin's lymphoma was observed among women who reported having had a suntan for less than 3 months per year and a suntan history of more than 60 years (odds ratio = 2.8, 95% confidence interval: 1.6, 4.9) compared with those who reported never having had a suntan. For women who reported having spent time in strong sunlight between 9 a.m. and 3 p.m. during the summer, a 70% increased risk of non-Hodgkin's lymphoma was observed for the highest tertile of duration compared with the lowest (odds ratio = 1.7, 95% confidence interval: 1.2, 2.4). The risk increased with increasing duration of time spent in strong sunlight in summer (p(trend) = 0.0051). The risk appears to vary by non-Hodgkin's lymphoma subtypes. Further investigations of the role of ultraviolet radiation on the risk of non-Hodgkin's lymphoma are warranted.

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)0.008
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.395
Threshold uncertainty score0.290

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.024
GPT teacher head0.339
Teacher spread0.315 · 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

Citations42
Published2007
Admission routes1
Has abstractyes

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