Are nicotine replacement strategies to facilitate smoking cessation safe?
Bibliographic record
Abstract
Professional obligations to curb the prevalence of cigarette smoking reflect the importance of this preventable risk factor for innumerable diseases. These include chronic obstructive pulmonary disease and oral, lung and other cancers, although the morbidity and mortality rates for cerebrovascular disease (e.g., ischemic strokes) and cardiovascular disease (e.g., ischemic myocardial infarction) tend to be greater. Various alternative nicotine sources (e.g., transdermal nicotine patches, nicotine gum, nicotine nasal sprays) have been incorporated into smoking cessation programs. This review is intended to increase professional awareness of nicotine delivery systems available in Canada, including safety considerations. The pathogenic potential of nicotine, regardless of source, and the contraindications to the use of nicotine replacement therapies are discussed. However, the systemic nicotine load in individuals undergoing replacement therapy is generally lower than during active smoking. Nicotine is only one of many thousands of constituents of tobacco smoke. Furthermore, nicotine replacement is usually delivered over the short term (a matter of weeks). Therefore, nicotine replacement is recognized as a relatively safe and effective aid to smoking cessation.
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 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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".