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Record W2600524949

Mobile Phone Use and Brain Tumour: An Age-Period-Cohort Analysis of Brain Tumour Rates in the Nordic Population

2014· article· en· W2600524949 on OpenAlexaff
Mehdi Ranjbaran, Mahmood Khodadost, Kamyar Mansori, Seyed Houssien Mousavi Jarrahi, Erfan Ayubi, Milad Nazarzadeh, Alireza Mosavi-Jarrahi

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicHealth, Environment, Cognitive Aging
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsMobile phonePeriod (music)CohortPopulationMedicinePhoneDemographyInternal medicineTelecommunicationsComputer scienceEnvironmental health
DOInot available

Abstract

fetched live from OpenAlex

Background: The association of the mobile phone use and risk of brain tumor remains controversial among radiationepidemiologists. Methods: We hypothesized if an association between brain tumor and mobile phone use exists, this association will be manifested as a cohort effect (as a proxy of association between mobile phone use and brain tumor) in the incidence rates of brain tumor during the period of 1990 to 2009. We used age-period-cohort methodology (generalized log-linear model) and compared the distribution of cohort effects in the observed rates of brain tumor from 1990 to 2009 to the cohort effects from rates driven based on epidemiological study results that reported a positive association between brain tumor and mobile phone use in the Nordic population. Three latency period of 1-4 years with odds ratio (OR) of 1.2, latency period of 5-9 years with OR of 1.3, and latency period of more than 10 years with OR of 2.7 were used to estimate expected rates. Result: the distribution of cohort effects between observed and expected rates were more similar among the males compared with females. A shorter latency was more consistent to observed rates. Conclusion: Our study supports a possible a weak association between mobile phone use and brain tumor; further fueling the controversies in association.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.119
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.113
GPT teacher head0.477
Teacher spread0.364 · 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.

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

Citations0
Published2014
Admission routes1
Has abstractyes

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