Estimating the Proportion of Cases of Lung Cancer Legally Attributable to Smoking: A Novel Approach for Class Actions Against the Tobacco Industry
Bibliographic record
Abstract
OBJECTIVES: The plaintiffs' lawyers for a class action suit, which was launched in Quebec on behalf of all patients with lung cancer whose disease was caused by cigarette smoking, asked us to estimate what proportion of lung cancer cases in Quebec, if they hypothetically could be individually evaluated, would satisfy the criterion that it is "more likely than not" that smoking caused the disease. METHODS: The novel methodology we developed is based on the dose-response relationship between smoking and lung cancer, for which we use the pack-years as a measure of smoking, and the distribution of pack-years of smoking among cases. RESULTS: We estimated that the amount of smoking required to satisfy the "more likely than not" criterion is between 3 and 11 pack-years. More than 90% of the Quebec cases satisfied even the most conservative of these thresholds. CONCLUSIONS: More than 90% of cases of lung cancer in Quebec are legally attributable to smoking. The methodology enhances the ability to conduct class action suits against the tobacco industry.
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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 | 0.027 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".