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Record W2321931906 · doi:10.18001/trs.2.2.7

Differences in Cigarette Design and Metal Content across Five Countries: Results from the International Tobacco Control (ITC) Project

2016· article· en· W2321931906 on OpenAlexaff
Rosalie Caruso, Brian V. Fix, James F. Thrasher, K. Michael Cummings, Geoffrey T. Fong, W. E. Stephens, Richard J. O’Connor

Bibliographic record

VenueTobacco Regulatory Science · 2016
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsUniversity of Waterloo
FundersNational Cancer Institute
KeywordsTobacco controlEnvironmental healthNicotinePopulationMedicineToxicologyPublic healthBiology

Abstract

fetched live from OpenAlex

OBJECTIVES: The current study examines physical cigarette design characteristics and tobacco metal content of cigarettes obtained from 5 countries to determine how these properties vary for cigarette brands, both within and across countries with different dominant manufacturers. METHODS: Cigarette packs were collected from International Tobacco Control Policy Evaluation Survey (ITC) participants in the U.S., the U.K., Mauritius, Mexico, and Thailand. Cigarettes were assessed for physical and design properties (eg, ventilation, pressure drop, rod density, weight) by published methods, and for metal content (As, Cd, Ni, Pb) by X-ray fluorescence spectrometry. RESULTS: Significant differences in cigarette design and toxic metal concentrations were observed between countries and between manufacturers within countries. Filter ventilation, which is strongly predictive of machine-measured tar and nicotine levels, varied most widely across countries. Ni and Cd were highest in Thailand (2.23ug/g and 1.64ug/g, respectively); As was highest in Mexico (0.29ug/g) and Pb was highest in the U.K. (0.43 ug/g). CONCLUSIONS: Parties to the FCTC should consider the adoption of uniform product standards related to cigarette design, emissions, and tobacco content that would reduce population health risks.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.413

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.067
GPT teacher head0.302
Teacher spread0.234 · 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
Published2016
Admission routes1
Has abstractyes

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