MétaCan
Menu
Back to cohort
Record W2112838811 · doi:10.1158/1055-9965.epi-13-1081

The Surgeon General Report on Smoking and Health 50 Years Later: Breast Cancer and the Cost of Increasing Caution

2014· review· en· W2112838811 on OpenAlexaff
Stanton A. Glantz, Kenneth C. Johnson

Bibliographic record

VenueCancer Epidemiology Biomarkers & Prevention · 2014
Typereview
Languageen
FieldMedicine
TopicCancer Risks and Factors
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMedicineBreast cancerSurgeon generalCohortDiseasePublic healthCausality (physics)Family medicinePassive smokingMeaning (existential)EpidemiologyCancerEnvironmental healthPathologyPsychologyInternal medicine

Abstract

fetched live from OpenAlex

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.

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)0.010
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.947
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.090
GPT teacher head0.439
Teacher spread0.348 · 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 designOther design
Domainnot available
GenreReview

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

Citations28
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueCancer Epidemiology Biomarkers & PreventionSame topicCancer Risks and FactorsFrench-language works237,207