Aspectos da mortalidade atribuível ao tabaco: revisão sistemática Aspects of tobacco attributable mortality: systematic review
Bibliographic record
Abstract
O artigo teve por objetivo analisar as metodologias publicadas e empregadas no cálculo da mortalidade atribuível ao fumo. Foram pesquisadas as bases de dados eletrônicas MEDLINE, LILACS entre 1990 e 2006. Foram encontrados 186 estudos que apresentaram a mensuração de mortalidade a partir do cálculo da fração atribuível ao fumo. Desses, foram selecionados 41 artigos. Os estudos realizados nos Estados Unidos e Canadá apresentaram metodologia uniformizada e taxas de mortalidade entre 18%-23%; 25%-29% no sexo masculino e 14%-17% no feminino. As variações metodológicas podem justificar as diferenças da mortalidade entre os estudos e nas estimativas para as principais doenças tabaco-relacionadas. The objective of the article was to assess methodologies published and applied in calculating mortality attributable to smoking. A review of the literature was made for the period 1990 to 2006, in the electronic databases MEDLINE and LILACS. A total of 186 studies were found, which measured mortality based on calculating the smoking-attributable risk. Of these, a total of 41 were selected. The studies that were carried out in the United States and Canada presented a more standard methodology and reported smoking attributable mortality to be 18%-23%, with male mortality being 25%-29% and female mortality 14%-17%. The variations can be attributed to methodological differences and to different estimates of the main tobacco-related illnesses.
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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 | 0.011 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.017 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".