A systematic review of preclinical studies evaluating the association between nicotine and the initiation and progression of cancer
Bibliographic record
Abstract
Background: The association between cigarette smoking and the increased risk of many cancers is well established. Conversely, epidemiological studies of smokeless tobacco demonstrate decreased risk, or no elevated risk, of certain cancers versus smoking. However, it is unclear what role, if any, nicotine plays in these associations. The objective of this systematic review was to synthesize the available evidence from preclinical studies that examined the potential association between nicotine and the initiation and/or progression of cancer. Methods: MEDLINE, Embase, PsychInfo, and Cochrane Database of Systematic Reviews were searched for articles published from inception until February 13, 2022. Studies were eligible for inclusion if they evaluated animal cancer or tumor models, compared nicotine and non-nicotine groups, and evaluated measures of cancer initiation or progression. Results: Among 1,137 identified articles, 61 were included in qualitative synthesis. Twelve studies reported data on tumor initiation, and 54 studies reported data on tumor progression. The majority of the tumor initiation studies did not identify an association between nicotine exposure and an increased risk of spontaneous tumor initiation. Results of tumor progression studies were inconsistent and varied across the reported measures, cancer type being evaluated, and animal cancer model used. Overall, the quality of reporting was poor, with many studies not demonstrating a high level of internal and/or external validity. Conclusions: In conclusion, although animal models have provided invaluable data for human health risk assessments of chemical exposures, the heterogeneity across the studies included in this systematic review make the interpretation and generalizability of the results difficult.
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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 | 0.013 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".