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Record W2737628399 · doi:10.1139/facets-2017-0014

Electronic cigarettes—A review of the physiological health effects

2017· article· en· W2737628399 on OpenAlexafffundvenue
Alyssa Zucchet, Grégory Schmaltz

Bibliographic record

VenueFACETS · 2017
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsUniversity of the Fraser ValleyUniversity of British Columbia
FundersUniversity of the Fraser Valley
KeywordsVapoursNicotineElectronic cigaretteMedicineCarcinogenAerosolPharmacologyToxicologyChemistryPathologyInternal medicineBiologyBiochemistry

Abstract

fetched live from OpenAlex

Electronic cigarettes (ECs) are devices that are used recreationally or as smoking cessation tools, and have become increasingly popular in recent years. We conducted a review of the available literature to determine the health effects caused by the use of these devices. A heating element in the EC aerosolizes a solution of propylene glycol, glycerol, nicotine (optional), and flavouring (optional). These compounds are generally harmless on their own. However, upon heating, they produce various carcinogens and irritants. We found that concentrations of these toxicants vary significantly depending on the type of EC device, the type of EC liquid, and the smoking behaviour of the user. Exposure to these vapours can cause inflammation and oxidative damage to in vitro and in vivo cells. EC aerosol can also potentially affect organ systems and especially cardiovascular and lung function. We concluded that EC use causes acute effects on health but not as severe as those of conventional cigarettes (CCs). These devices could, therefore, be of use for smokers of CCs wishing to quit. However, as EC aerosol introduces new toxicants not found in CCs, long-term studies are needed to investigate possible chronic effects associated with EC use.

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 score0.328
Threshold uncertainty score0.117

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 head0.035
GPT teacher head0.358
Teacher spread0.323 · 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

Citations11
Published2017
Admission routes3
Has abstractyes

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