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Record W4223524446 · doi:10.1038/s41533-022-00277-9

Global youth vaping and respiratory health: epidemiology, interventions, and policies

2022· review· en· W4223524446 on OpenAlexaff
Lynnette Nathalie Lyzwinski, John A. Naslund, Christopher J. Miller, Mark J. Eisenberg

Bibliographic record

Venuenpj Primary Care Respiratory Medicine · 2022
Typereview
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsMcGill UniversityJewish General Hospital
FundersUniversity of Queensland
KeywordsMedicinePublic healthEnvironmental healthPsychological interventionPopulationEpidemiologyIntervention (counseling)BronchitisAsthmaPsychiatryNursingPathologyImmunology

Abstract

fetched live from OpenAlex

E-cigarette usage (also known as e-cigarettes or vaping products) has increasingly been recognized as a global public health problem. One challenge in particular involves their marketing to minors (teenagers and children) and the rising prevalence of use in this population. E-cigarettes unnecessarily expose minors to health risks, these include respiratory health problems, such as exacerbations of asthma, bronchitis, and respiratory-tract irritation. Nicotine, commonly found in e-cigarettes, is also associated with cognitive impairment and neurodevelopmental problems. E-cigarettes are also risk factors for downstream substance use, including cigarettes and cannabis initiation (the gateway hypothesis), which compounds health risks in dual users. Current public health preventative and intervention studies are limited, and there is a clear need for more interventions that may prevent usage and assist with cessation in this vulnerable population. Physician education and screening uptake should also be enhanced. Stricter public health policy and protection measures are also needed on a global scale to limit e-cigarette exposure in minors.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.939
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.355
GPT teacher head0.479
Teacher spread0.123 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations98
Published2022
Admission routes1
Has abstractyes

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