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Record W2724971699 · doi:10.1097/cej.0000000000000403

Is there an association between trends in alcohol consumption and cancer mortality? Findings from a multicountry analysis

2017· article· en· W2724971699 on OpenAlexafffund
Naomi Schwartz, Diane Nishri, Sandrene Chin Cheong, Norman Giesbrecht, Julie Klein-Geltink

Bibliographic record

VenueEuropean Journal of Cancer Prevention · 2017
Typearticle
Languageen
FieldMedicine
TopicAlcohol Consumption and Health Effects
Canadian institutionsCentre for Addiction and Mental HealthCancer Care Ontario
FundersCentre for Addiction and Mental HealthCancer Care Ontario
KeywordsPer capitaMedicineEnvironmental healthAlcohol consumptionAlcoholConsumption (sociology)DemographyBladder cancerCancerMortality rateSurgeryPopulationInternal medicineBiology

Abstract

fetched live from OpenAlex

The aim of this analysis is to examine long-term trends in alcohol consumption and associations with lagged data on specific types of cancer mortality, and indicate policy implications. Data on per capita annual sales of pure alcohol; mortality for three alcohol-related cancers - larynx, esophageal, and lip, oral cavity, and pharynx; and per capita consumption of tobacco products were extracted at the country level. The Unobservable Components Model was used for this time-series analysis to examine the temporal association between alcohol consumption and cancer mortality, using lagged data, from 17 countries. Statistically significant associations were observed between alcohol sales and cancer mortality, in the majority of countries examined, which remained after controlling for tobacco use (P<0.05). Significant associations were observed in countries with increasing, decreasing, or stable trends in alcohol consumption and corresponding lagged trends in alcohol-related cancer mortality. Curtailing overall consumption has potential benefits in reducing a number of harms from alcohol, including cancer mortality. Future research and surveillance are needed to investigate, monitor, and quantify the impact of alcohol control policies on trends in cancer mortality.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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 score0.091
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.160
GPT teacher head0.464
Teacher spread0.304 · 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

Citations13
Published2017
Admission routes2
Has abstractyes

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