E-cigarettes: effective cessation tools or public health threat?
Bibliographic record
Abstract
In a short time, electronic cigarettes have become a multi-billion dollar industry. Since their introduction to the market, prevalence of ever-use among smokers in the USA appears to have increased from ∼2% in 2010 to >30% in 2012, and the rate of increase appears to be similar in the United Kingdom, Ireland and other Western countries according to a special Eurobarometer survey in 2014.1 The e-cigarette market is estimated to be worth over $3 billion. However, there is no consensus on the role of e-cigarettes and their contribution to the provision of smoking cessation (SC) services, nor to global tobacco control. At the Conference of the Parties to the WHO Framework Convention on Tobacco Control in September 2014, the secretariat presented a report outlining the current facts concerning e-cigarettes, put forth an opinion on these devices, and offered considerations on options for regulation.2 This was on the tail of two contrasting letters to Margaret Chan, Director General of the WHO, submitted by scientists from many disciplines including tobacco control, public health, epidemiology, pharmacology and the clinical sciences (Letters to Dr Chan; June 2014). One letter stated that e-cigarettes offer huge prospective gains by reducing the prevalence of cigarette smoking and subsequently, the harm done by smoking. Those in favour of this position therefore requested support for the introduction and widespread availability of e-cigarettes. The other letter cautioned restraint, citing the possible damage that e-cigarettes could have on tobacco control.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.016 | 0.012 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".