“Electronic Cigarettes” Are Not Cigarettes, and Why That Matters
Bibliographic record
Abstract
As the prevalence rates of cigarette use have declined over the past decade, use of electronic cigarettes (e-cigarettes) continues to increase, and companies are heavily invested in manufacturing new e-cigarette products. Scientists are therefore studying e-cigarette use at a rapid rate, generally by conceptualizing e-cigarettes as similar to traditional cigarettes in their use and effects. Thinking of e-cigarettes as largely comparable with cigarettes, however, fails to capture the unique e-cigarette capabilities, user experiences, and effects on nicotine dependence and even health. Assuming that e-cigarette users puff on their devices as they do cigarettes to attain doses of nicotine comparable in magnitude and asking questions about e-cigarette use modeled after how smoking behavior has been usually assessed (eg, puff number, duration, number of cigarettes per day) may miss important differences. A greater appreciation of the distinct uniqueness of e-cigarettes, as compared with cigarettes, will help to accelerate innovative research on e-cigarettes and other electronic devices, leading to new theoretical models and behavioral measures. IMPLICATIONS: With research about electronic cigarettes (e-cigarettes) rapidly increasing, this commentary addresses the conceptualization of e-cigarettes as similar to traditional cigarettes. The more we attempt to understand and measure e-cigarettes as equivalent to cigarettes, the more likely research may err in conclusions about these unique devices. Our commentary notes how using unique conceptualizations and measures for e-cigarettes will help accelerate new research.
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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.017 | 0.019 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".