An Overview of E-Cigarette Impact on Reproductive Health
Bibliographic record
Abstract
Electronic cigarettes (e-cigarettes) are often considered a "safe substitute" for conventional cigarette cessation. The composition of the fluid is not always clearly defined and shows a large variation within brands and manufacturers. More than 80 compounds were detected in liquids and aerosols. E-cigarettes contain nicotine, and the addition of flavorings increases the toxicity of e-cigarette vapour in a significant manner. The heat generated by the e-cigarette leads to the oxidation and decomposition of its components, eventually forming harmful constituents in the inhaled vapour. The effects of these toxicants on male and female reproduction are well established in conventional cigarette smokers. Although toxins were measured at much lower levels in e-cigarette aerosols compared to smoke from a conventional cigarette, there are concerns about their potential impact on male and female reproduction. The information available was mainly obtained from studies conducted in animal models, and investigations in humans are scarce. However, the effects observed in animal models suggest that caution should be taken when vaping and that more research needs to be conducted to identify its potential adverse effects on fertility. The prevalence of e-cigarette usage is alarming, and warnings should be made about the impact of vaping on reproductive health. This document reviews the data regarding the impact of e-cigarette use on male and female reproduction.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".