Acute Cardiovascular Effects of Vaping Compared to Cigarette Smoking in Young Adults
Bibliographic record
Abstract
Background: First introduced as the safer alternative to smoking, vaping has become a popular activity among young adults. However, little is known about the potential health effects of vaping. This pilot project examined the acute cardiovascular effects of nicotine vapes/e-cigarettes (EC) in comparison to tobacco cigarettes (TC) in young adults in order to determine if vaping is more detrimental to cardiovascular health than traditional cigarettes. Methods: 16 healthy participants (7 M, 9 F; 20.2 ± 1.9 years) were recruited to participate in the study. Anthropometric measures were determined upon entry into the study. Circulatory measures (heart rate [HR], blood pressure [BP] and heart rate variability [HRV]) were measured prior to and 10-min following vaping or cigarette smoking and in response to an orthostatic challenge. Results: Resting circulatory and HRV measures were not different between chronic EC-users and TC-smokers. Vaping and cigarette smoking induced a significant increase in cardiovascular measures (HR and BP) but not HRV measures. Both groups responded similarly to the orthostatic challenge prior to and following vaping/smoking. Conclusion: These results indicate that, from a cardiovascular perspective, vaping induces similar acute effects as cigarette smoking and that young adults should be counselled about these adverse effects accordingly.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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 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".