New Tobacco and Tobacco-Related Products: Early Detection of Product Development, Marketing Strategies, and Consumer Interest
Bibliographic record
Abstract
BACKGROUND: A wide variety of new tobacco and tobacco-related products have emerged on the market in recent years. OBJECTIVE: To understand their potential implications for public health and to guide tobacco control efforts, we have used an infoveillance approach to identify new tobacco and tobacco-related products. METHODS: Our search for tobacco(-related) products consists of several tailored search profiles using combinations of keywords such as "e-cigarette" and "new" to extract information from almost 9000 preselected sources such as websites of online shops, tobacco manufacturers, and news sites. RESULTS: Developments in e-cigarette design characteristics show a trend toward customization by possibilities to adjust temperature and airflow, and by the large variety of flavors of e-liquids. Additionally, more e-cigarettes are equipped with personalized accessories, such as mobile phones, applications, and Bluetooth. Waterpipe products follow the trend toward electronic vaping. Various heat-not-burn products were reintroduced to the market. CONCLUSIONS: Our search for tobacco(-related) products was specific and timely, though advances in product development require ongoing optimization of the search strategy. Our results show a trend toward products resembling tobacco cigarettes vaporizers that can be adapted to the consumers' needs. Our search for tobacco(-related) products could aid in the assessment of the likelihood of new products to gain market share, as a possible health risk or as an indicator for the need on independent and reliable information of the product to the general public.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".