E-cigarettes: effective cessation tools or public health threat?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it