Tobacco Industry Strategies to Minimize or Mask Cigarette Smoke: Opportunities for Tobacco Product Regulation
Bibliographic record
Abstract
INTRODUCTION: The tobacco industry has developed technologies to reduce the aversive qualities of cigarette smoke, including secondhand smoke (SHS). While these product design changes may lessen concerns about SHS, they may not reduce health risks associated with SHS exposure. Tobacco industry patents were reviewed to understand recent industry strategies to mask or minimize cigarette smoke from traditional cigarettes. METHODS: Patent records published between 1997 and 2008 that related to cigarette smoke were conducted using key word searches. The U.S. Patent and Trademark Office web site was used to obtain patent awards, and the World Intellectual Property Organization's Patentscope and Free Patents Online web sites were used to search international patents. RESULTS: The search identified 106 relevant patents published by Japan Tobacco Incorporated, British America Tobacco, Philip Morris International, and other tobacco manufacturers or suppliers. The patents were classified by their intended purpose, including reduced smoke constituents or quantity of smoke emitted by cigarettes (58%, n = 62), improved smoke odor (25%, n = 26), and reduced visibility of smoke (16%, n = 18). Innovations used a variety of strategies including trapping or filtering smoke constituents, chemically converting gases, adding perfumes, or altering paper to improve combustion. CONCLUSIONS: The tobacco industry continues to research and develop strategies to reduce perceptions of cigarette smoke, including the use of additives to improve smoke odor. Surveillance and regulatory response to industry strategies to reduce perceptions of SHS should be implemented to ensure that the public health is adequately protected.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".