Hundred years of cigarette smoking in Poland: three phases of the tobacco epidemic
Bibliographic record
Abstract
After the World War II Poland experienced a rapid growth of cigarette consumption.Between 1949 and 1980, tobacco sales increased from 21.8 billion cigarettes per annum to 94.2 billion.By the 1970s cigarette production, per capita sales, and smoking prevalence among Poles aged 15+ were among the highest in the world.The tobacco industry became a strategic branch of the economy.By the early 1980s circa 15 million Poles smoked (10.5 million males and 4.5 million females).This prolonged exposition to tobacco smoke after several decades led to an explosion of tobacco-related diseases.Between 1980 and 2000 Poland first experienced stagnation in smoking rates, and then a sustained decline in both sexes.This has been attributed to the collapse of the tobacco industry in the 1980s, and the intervention programmes stemming from the Polish Anti-tobacco Law of 1995, as well as the activity of pro-health civil society groups and medical associations.Smoking prevalence in Poland declined between 1970s and 2014 from 73% to 28% among men and from 30% to 19% in women.The annual decline rates in Poland in this period belonged to the fastest in the world.This positive trend led to the curbing, and later fall in rates of tobacco-related diseases.In the years 1990-2015 lung cancer mortality rates among middle-aged men (35-54 years) declined from 60/100,000 to 20/100,000 and in female population between 2005 and 2015 from 15/100,000 to 11/100,000.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".