Principles for effective tobacco warning systems: the USA gets a failing grade
Bibliographic record
Abstract
The US Food and Drug Administration (FDA) has introduced its new tobacco warning system after 35 years of weak, stale warnings. And now, ostensibly due to the COVID-19 pandemic, the FDA has delayed the ‘effective date’ of the new warnings1 until October 16, 2021. Superior warnings have been required on American products sold internationally for more than two decades. Despite the criticism below, the improved text, size and positioning of the new warnings will save thousands of lives. However, they offer little worth replicating elsewhere. The critique that follows draws from my interest in tobacco warnings developed while heading Canada’s Non-Smokers’ Rights Association (NSRA) from 1976-2012. The NSRA led campaigns for Canada’s landmark tobacco advertising ban (1988) and for global precedent-setting package warnings (1994 and 2001). These reforms triggered tobacco-related law reform around the world and undoubtedly led to the NSRA being cited in 2000 as the inaugural recipient of the American Cancer Society’s international Luther L Terry Award in the 'Outstanding Organization' category. Given this history with warnings, I believe that a strong tobacco product warning system incorporates at least five essential elements: Tort law requires manufacturers in the USA, Canada and other countries to warn both of the nature of the risks of their products and the magnitude of the danger caused.2 When designing tobacco warning systems, governments should not adopt a standard for warnings below what is considered acceptable in law for other products. Unfortunately, the USA has done precisely that. Almost every warning system on the globe has a warning of lung cancer, but this key warning did not make the final list of American warnings. In contrast, Canada had two effective lung cancer warnings in the 2001 phase of its warnings. One read ‘WARNING CIGARETTES CAUSE LUNG CANCER’. The subtext spelled out the …
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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 | 0.039 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.026 |
| Scholarly communication | 0.020 | 0.019 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.030 | 0.045 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".