IASO Policy Briefing The prevention of obesity and NCDs: Challenges and opportunities for governments
Bibliographic record
Abstract
The prevention of obesity and NCDs: challenges and opportunities for governments “World Health Organization data show that rates of obesity nearly doubled in every region of the world from 1980 to 2008. Worldwide, one in three adults has raised blood pressure. One in ten adults has diabetes. These are the diseases that tax health systems to the breaking point. These are the diseases that break the bank. These are the diseases that can cancel out the gains of modernization and development. These are the diseases that can set back poverty alleviation, pushing millions of people below the poverty line each year.” Margaret Chan, Director General, World Health Organization, May 2012 The major non-communicable diseases (NCDs) – cancer, cardiovascular disease, diabetes and chronic pulmonary disease now account for more than 36 million deaths (65 % of all deaths) every year. Most of these deaths occur in low- and middle-income countries, almost a quarter of which occur in people under age 60 years. 1 By 2030, NCDs are expected to cause for four times as many deaths as the combined figure for infectious diseases, and maternal, perinatal and malnutritionrelated conditions. 2 Tobacco, alcohol, poor diets and sedentary behaviour lie behind much of the disease burden. The rapid rise in obesity prevalence worldwide indicates that diet and lack of
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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 | 0.019 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.048 | 0.023 |
| Insufficient payload (model declined to judge) | 0.030 | 0.017 |
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".