MétaCan
Menu
Back to cohort

Blowing smoke: the history of tobacco-specific nitrosamines in Canadian tobacco

2016· article· en· W2414806198 on OpenAlexaffabout
Neil Collishaw

Bibliographic record

VenueTobacco Control · 2016
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsPhysicians for a Smoke-Free Canada
Fundersnot available
KeywordsSmokeTobacco controltar (computing)Secondhand smokeTobacco smokeCuring (chemistry)BusinessEnvironmental healthMedicineChemistryPublic healthWaste managementEngineering

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.951

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.027
GPT teacher head0.251
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueTobacco ControlSame topicSmoking Behavior and CessationFrench-language works237,207