Study protocol of EUREST-PLUS - European Regulatory Science on Tobacco: Policy Implementation to Reduce Lung Disease
Bibliographic record
Abstract
Efforts to mitigate the devastation of tobacco-attributable morbidity and mortality in the European Union (EU) are founded on its newly adopted Tobacco Products Directive (TPD) along with the first-ever health treaty, the WHO Framework Convention on Tobacco Control (FCTC).The aim of this Horizon 2020 Project entitled European Regulatory Science on Tobacco: Policy Implementation to Reduce Lung Disease (EUREST-PLUS) is to monitor and evaluate the impact of the implementation of the TPD across the EU, within the context of WHO FCTC ratification.To address this aim, EUREST-PLUS consists of four objectives: 1) To create a cohort study of 6000 adult smokers in six EU MS (Germany, Greece, Hungary, Poland, Romania, Spain) within a pre-TID vs post-TPD implementation study design; 2) To conduct secondary dataset analyses of the Special Eurobarometer on Tobacco Survey (SETS); 3) To document changes in e-cigarette product parameters (technical design, labelling/packaging and chemical composition) pre-TID vs post-TPD; and 4) To enhance innovative joint research collaborations on chronic non-communicable diseases.Through this methodological approach, EUREST-PLUS is designed to generate strong inferences about the effectiveness of tobacco control policies, as well as to elucidate the mechanisms and factors by which policy implementation translates to population impact.Findings from EUREST-PLUS have potential global implications for the implementation of innovative tobacco control policies and its impact on the prevention of lung diseases.
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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 | 0.053 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.105 | 0.030 |
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".