Heat-not-burn tobacco products: an emerging threat to cardiovascular health
Bibliographic record
Abstract
Cigarette smoking is at all-time lows globally, but the use of electronic cigarettes has increased profoundly. Recent reports of electronic cigarette or vaping use-associated lung injury may lead individuals to explore novel methods of nicotine consumption, such as heat-not-burn devices. IQOS from Philip Morris, a heat-not-burn device, became available for purchase in the United States in October 2019. Philip Morris claims that 8.8 million people have abandoned traditional cigarettes in favor of IQOS; however, evidence suggests that it may act as a gateway or complement to cigarette smoking, rather than a replacement. Surveys indicate that 96% of Korean IQOS users also smoke cigarettes, and 45% of Italian users of IQOS had never smoked cigarettes. In the United States, Canada, and England, susceptibility of youth to trying IQOS was slightly lower than electronic cigarettes, but higher than cigarette smoking. Heat-not-burn products produce mainstream and second-hand emissions of harmful chemicals, including nicotine, particulate matter, benzene, acrolein, and tobacco-specific nitrosamines. The levels of these emissions, despite being less than those of traditional cigarettes, are potentially harmful to cardiovascular health. A study of current smokers showed similar acute effects of heat-not-burn tobacco products and traditional cigarettes on heart rate, blood pressure, and arterial stiffness. Rats exposed to IQOS had similar vascular endothelial function impairment to those exposed to cigarettes. Heat-not-burn aerosol exposure of cultured macrophages elicited increased oxidative stress, although less than that induced by cigarette smoke. Further studies are needed to better understand the cardiovascular effects of heat-not-burn tobacco products.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".