Tobacco Smoking: Patterns, Health Consequencesfor Adults, and the Long-term Health of the Offspring
Bibliographic record
Abstract
Tobacco use started several centuries ago and increased markedly after the invention of the cigarette making machine. Once people start smoking they find it difficult to quit the habit. This is due to the addictive effect of nicotine in tobacco smoke. Various epidemiologic and laboratory studies clearly showed that smoking is associated with various diseases such as heart diseases, asthma and emphysema and the associated increase in morbidity and mortality of smokers. Several studies implicate nicotine as the causative factor in tobacco smoke. Apart from nicotine, various carcinogens also occur in tobacco smoke resulting in an increase in the incidence of cancer in smokers. While the smoking habit is decreasing in developed countries, tobacco use increases in the developing countries. Smoking prevalence is also highest in poor communities and amongst those with low education levels. It is important to note that, although ther is a decline in the number of smokers in the developed countries, there is a three to four decades lag between the peak in smoking prevalence and the subsequent peak in smoking related mortality. It has been shown that maternal smoking induces respiratory diseases in the offspring. There is also evidence that parental smoking may program the offspring to develop certain diseases later in life. Various studies showed that maternal nicotine exposure during pregnancy and lactation via tobacco smoke of nicotine replacement therapy (NRT), program the offspring to develop compromised lung structure later in life with the consequent compromised lung function. This implies that NRT is not an option to assist pregnant or lactating smokers to quit the habit. Even paternal smoking may have an adverse effect on the health of the offspring since it has been shown that 2nd and 3rd hand smoking have adverse health consequences for those exposed to it.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".