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Record W3019125512 · doi:10.1097/mop.0000000000000889

Vaping implications for children and youth

2020· review· en· W3019125512 on OpenAlexaff
Meghan Gilley, Suzanne Beno

Bibliographic record

VenueCurrent Opinion in Pediatrics · 2020
Typereview
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of TorontoBC Children's HospitalUniversity of British Columbia
Fundersnot available
KeywordsLegislationEnvironmental healthProduct (mathematics)PsychologyBusinessOccupational safety and healthAddictionPublic healthMedicinePolitical sciencePsychiatryNursing

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: The development and uptake of E cigarettes are a relatively recent phenomenon. Because of aggressive marketing, attractive designs, enticing flavors and primarily reactionary legislation, we are now seeing soaring rates of adolescent vaping with associated consequences. This review explores how E cigarettes work, their health implications, epidemiology among youth and current regulatory strategies. RECENT FINDINGS: Recently, the Center for Disease Control and Prevention reported that 27% of high school students had used a tobacco product within the last month, the majority being E-cigarettes in 20.8% of high school students. Vaping has managed to reverse a decades long trend of declining nicotine use among youth. Long-term addiction is not the only concern related to youth vaping; there are also increasing reports of short-term health consequences, such as seizures, acute nicotine toxicity, burns and lung injury. SUMMARY: Industry has created and aggressively marketed a product that is enticing to adolescents. E cigarettes have sleek designs, desirable flavors and social acceptability with perceived safety among youth. This has resulted in epidemic E cigarette use in youth with resultant significant short-term and long-term health concerns. Legislation must include regulations that strictly avoid marketing and sales to youth, as well as reducing access to these products.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.964
Threshold uncertainty score0.820

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.184
GPT teacher head0.436
Teacher spread0.252 · 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 designOther design
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

Citations22
Published2020
Admission routes1
Has abstractyes

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