Verification of the prognosis of lung cancer mortality in Poland based on data about smoking habits
Bibliographic record
Abstract
Introduction Lung cancer is the most common cancer among males worldwide and one of the most common cancers among females. Poland is among European countries of the high risk of lung cancer for men. Although there are several factors influencing the risk of developing lung cancer, tobacco smoking is well established as the main risk factor. Thus, changes in lung cancer mortality may reflect changes in smoking habits in a given population. Methods The population data and its forecast up to 2030 come from the Central Statistical Office and the lung cancer mortality data from the Department of Epidemiology of Maria Skłodowska Institue – Oncology Centre. The frequencies of smoking habits were smoothed based on the survey done by Central Statistical Office in 1996, 2004, 2009, 2014. The present model was based on model-based smoothing of the smoking habit – specific risk ratios estimated for males and females in Europe. Results Among men, the scenario in which about 20% of smokers quit smoking every 5 years turned out to be too pessimistic. The real change in lung cancer mortality turned out to be deeper. In 2015, lung cancer mortality among men was more than 25% lower than it was from the forecast. Among women, a scenario in which only 10% quits smoking works. This is too little to reduce women's lung cancer mortality. Conclusions The results obtained clearly indicate that cutting down on the number of smokers translates directly into a considerable reduction of the lung cancer incidence rate. Lung cancer is a disease to be easily avoided. Smoking cessation is the best way to reduce risk of lung cancer among human population.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".