Mortality and years of life lost by colorectal cancer attributable to physical inactivity in Brazil (1990–2015): Findings from the Global Burden of Disease Study
Bibliographic record
Abstract
INTRODUCTION: The aims of this study were to estimate all-cause and cause-specific mortality and years of life lost, investigated by disability-adjusted life-years (DALYs), due to colorectal cancer attributable to physical inactivity in Brazil and in the states; to analyze the temporal trend of these estimates over 25 years (1990-2015) compared with global estimates and according to the socioeconomic status of states of Brazil. METHODS: Databases from the Global Burden of Disease Study (GBD) for Brazil, Brazilian states and global information were used. It was estimated the total number and the age-standardized rates of deaths and DALYs for colorectal cancer attributable to physical inactivity in the years 1990 and 2015. We used the Socioeconomic Development Index (SDI). RESULTS: Physical inactivity was responsible for a substantial number of deaths (1990: 1,302; 2015: 119,351) and DALYs (1990: 31,121; 2015: 87,116) due to colorectal cancer in Brazil. From 1990 to 2015, the mortality and DALYs due to colorectal cancer attributable to physical inactivity increased in Brazil (0.6% and 0.6%, respectively) and decreased around the world (-0.8% and -1.1%, respectively). The Brazilian states with better socioeconomic indicators had higher rates of mortality and morbidity by colorectal cancer due to physical inactivity (p<0.01). Physical inactivity was responsible for deaths and DALYs due to colorectal cancer in Brazil. CONCLUSIONS: Over 25 years, the Brazilian population showed more worrisome results than around the world. Actions to combat physical inactivity and greater cancer screening and treatment are urgent in the Brazilian states.
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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 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| 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".