Evidence update on the cancer risk of vaping e-cigarettes: A systematic review
Bibliographic record
Abstract
INTRODUCTION: There is substantial interest in the association of vaping e-cigarettes with the risk of cancer. We analyzed this risk in different populations by updating the Kings College London (KCL) review to include the period between July 2021 and December 2023. METHODS: original studies examining the association between e-cigarettes and cancer risk, but we excluded qualitative studies. We summarized findings on three types of e-cigarette exposure: acute, short- to medium-term, and long-term. Additionally, we assessed whether the health effects differ between subgroup populations based on various sociodemographic factors, for which we also screened the previously included studies in the KCL review. Different risk-of-bias tools were used to assess the quality of the included human studies. RESULTS: and animal studies. All human studies were conducted in adults, and about half of them had a low risk of bias. No significant incident or prevalent risk of lung cancer or other types of cancer was found in the never smoker current vapers population. However, there was substantial biomarker-based evidence of a significant association between e-cigarette exposure and oxidative stress, cellular apoptosis, DNA damage, genotoxicity, and tumor growth, particularly following acute exposure. We did not find any age or sex-based differences in cancer risk, and findings on race and education-based differences were insufficient. CONCLUSIONS: There is substantial evidence that e-cigarette exposure is associated with biomarkers reflective of cancer disease risk. However, the overall evidence on cancer risk is still limited and should be further investigated by future research, particularly rigorously designed clinical trials and population-based research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".