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Record W2594539003 · doi:10.1177/1010539517696552

Rolling Tobacco in Banana Leaves, Newspaper, or Copybook Paper Associated With Significant Reduction in Lung Function in Vanuatu

2017· article· en· W2594539003 on OpenAlexaff
Charles A. Weitz, Kathryn M. Olszowy, Kelsey N. Dancause, Cheng Sun, Alysa Pomer, Howard Silverman, G Lee, Len Tarivonda, Chim W. Chan, Akira Kaneko, J. Koji Lum, Ralph M. Garruto

Bibliographic record

VenueAsia Pacific Journal of Public Health · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsUniversité du Québec à Montréal
FundersWenner-Gren Foundation
KeywordsNewspaperSmokeLung functionTobacco smokeVital capacityMedicineTobacco useEnvironmental healthToxicologyAdvertisingHorticultureGeographyDemographyBusinessLungBiologyMeteorologySociologyDiffusing capacityInternal medicine

Abstract

fetched live from OpenAlex

In addition to the widespread availability of packaged cigarettes, the inhabitants of island nations of the Southwest Pacific frequently smoke commercially available loose tobacco using manufactured rolling papers, as well as locally grown tobacco rolled in manufactured rolling paper or wrapped in leaves, copybook paper, and newspaper. In this study, Vanuatu men who smoked local tobacco rolled in leaves, copybook paper, or newspaper showed significantly lower forced vital capacity (FVC), forced expiratory volume in 1 second (FEV 1 ), and FEV 1 /FVC ratios than men who smoked packaged cigarettes, store-bought tobacco rolled in manufactured rolling paper, or who smoked locally grown tobacco rolled in manufactured rolling papers. The addition of toxins from these unusual tobacco-wrapping media produces lung function deficits similar to the pattern noted among tobacco smokers who also inhale smoke from burning biomass. Thus, public health initiatives should consider including strategies addressing the use of wrapping media among smokers in South Pacific island societies.

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)0.009
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 score0.103
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.001
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 head0.072
GPT teacher head0.321
Teacher spread0.249 · 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

Citations5
Published2017
Admission routes1
Has abstractyes

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