The Surgeon General Report on Smoking and Health 50 Years Later: Breast Cancer and the Cost of Increasing Caution
Bibliographic record
Abstract
Despite the Surgeon General's strong track record and the rapidly expanding body of solid scientific work demonstrating that smoking caused a wide range of diseases, the decision making process for concluding "causality" in Surgeon General reports has become increasingly cautious and defensive. Whereas, the 1964 report did not conclude that smoking caused heart disease, it recommended that "from the public health viewpoint [one should] assume that the established association has causative meaning rather than to suspend judgment until no uncertainty remains," the de facto practice has become to do just the opposite. In particular, the 2004 report reached an affirmative negative conclusion that active smoking did not cause breast cancer and the 2006 report on passive smoking only found the link "suggestive." In contrast, in 2005 the California EPA found both active and passive smoking caused breast cancer in younger women. The evidence has continued to strengthen since 2005: there are now 12 large cohort studies that consistently demonstrate a dose-response relationship with smoking before first birth and increased breast cancer risk. The Surgeon General's increasing caution is preventing young women around the world from appreciating the risks that smoking and secondhand smoke pose for developing breast cancer.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".