Mortality and years of life lost due to breast cancer attributable to physical inactivity in the Brazilian female population (1990–2015)
Bibliographic record
Abstract
The aims of this study were as follows: to estimate the mortality and years of life lost, assessed by disability-adjusted life years (DALYs), due to breast cancer attributable to physical inactivity in Brazilian women; to compare the estimates attributable to physical inactivity and to other modifiable risk factors; and to analyse the temporal evolution of these estimates within Brazilian states over 25 years (1990-2015), compared with global estimates. Databases from the Global Burden of Disease Study for Brazil, Brazilian states, and other parts of the world were used. Physical inactivity has contributed to a substantial number of deaths (1990: 875; 2015: 2,075) and DALYs (1990: 28,089; 2015: 60,585) due to breast cancer in Brazil. Physical inactivity was responsible for more deaths and DALYs (~12.0%) due to breast cancer than other modifiable risk factors (~5.0%). The Brazilian states with better socioeconomic indicators had higher age-standardized rates of mortality and morbidity due to breast cancer attributable to physical inactivity. From 1990 to 2015, mortality due to breast cancer attributable to physical inactivity increased in Brazil (0.77%; 95%U.I.: 0.27-1.47) and decreased (-2.84%; 95%U.I.: -4.35 - -0.10) around the world. These findings support the promotion of physical activity in the Brazilian female population to prevent and manage breast cancer.
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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 | 0.001 | 0.003 |
| 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 | 0.000 | 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".