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Record W2160414530 · doi:10.3899/jrheum.090211

Basic Concepts of Enthesis Biology and Immunology

2009· article· en· W2160414530 on OpenAlexvenueno aff
Michael Benjamin, Dennis McGonagle

Bibliographic record

VenueJournal of Rheumatology Supplement · 2009
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDemographyOddsLogistic regressionEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

<h3>Introduction</h3> Heated tobacco products (HTPs) have been available in the Korean market since June 2017. In this study, we examined the prevalence and predictors of HTP use among Korean adolescents and the association between HTP and electronic cigarette (EC) use and attempts to quit conventional cigarette (CC) smoking. <h3>Methods</h3> We analysed the data of a representative sample (n=60 040) of 13–18-year-old middle-school and high-school students in Korea who had participated in the 14th Korea Youth Risk Behavior Web-based Survey in 2018. <h3>Results</h3> The prevalence of ever HTP use among Korean adolescents was 2.9% (men: 4.4%, women: 1.2%), a year after the introduction of HTPs in the Korean market. Furthermore, 81.3% of the 1568 ever HTP users were triple users of HTPs, ECs and CCs. Multivariate analysis revealed that ever HTP use was greater among men, higher-grade students, current CC and/or EC users and risky alcohol drinkers. Among current CC smokers, ever users of ECs (28%–30%) and ever HTP users and current EC users (48%) were more likely to have attempted to quit CC smoking than those who had never used HTPs and ECs. However, there were fewer HTP and/or EC ever users among ever CC smokers who successfully quit smoking. <h3>Conclusions</h3> Many adolescents, especially CC and EC users, had already used HTPs shortly after the introduction of HTPs in Korea. The use of newer types of tobacco products is associated with lower odds of abstinence from CCs; therefore, it is important to protect adolescents from them.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.020
Threshold uncertainty score0.528

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.016
GPT teacher head0.331
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

Citations36
Published2009
Admission routes1
Has abstractyes

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