Blowing smoke: the history of tobacco-specific nitrosamines in Canadian tobacco
Bibliographic record
Abstract
OBJECTIVE: To demonstrate how changes in tobacco flue-curing practices in the 20th century increased levels of tobacco-specific nitrosamines (TSNAs) in tobacco smoke. METHODS: Previously undisclosed documents and testimony made public as a result of a class action trial against tobacco companies in Montreal, Canada, were reviewed for information on TSNAs and tobacco curing practices. These were combined with other pertinent documents to form the basis for a comprehensive historical review of TSNAs and tobacco curing practices. RESULTS: In the 1960s and 1970s, a change was made from indirect heating to direct heating for flue-curing tobacco that resulted in an increase in the TSNA-to-tar ratio in flue-cured tobacco. This occurred in both Canada and the USA. When this change was made, tobacco companies did not monitor for increased levels of TSNAs and did not study possible adverse effects on human health. As a result, smokers were unknowingly exposed to unnecessarily high levels of TSNAs for 30-40 years. In recent years, tobacco companies have changed curing practices back to indirect heating, thus returning the TSNA-to-tar ratios in tobacco smoke to their previously low levels. CONCLUSIONS: In view of this information brought to light in this paper, any claims by tobacco companies that they were acting prudently by lowering TSNA levels are unwarranted. They fail to acknowledge that it was their actions that raised TSNA levels in the first place about half a century ago.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".