Bibliographic record
Abstract
Obesity is now commonly defined in adults as a BMI > 30 kg/m2. The prevalence of obesity in established market economies (Europe, USA, Canada, Australia, etc.) varies greatly, but a weighed estimate suggests an average prevalence in the order of 15-20%. The prevalence in these countries generally shows increasing trends over time. Obesity is also relatively common in Latin America, but much less so in sub-Saharan Africa and Asia where the majority of the world population lives. Nevertheless obesity rates are increasing there as well and, more importantly, rates of diabetes are increasing even more quickly, particularly in Asian countries. The risks of type 2 diabetes mellitus in these countries tend to increase sharply at levels of BMI generally classified as acceptable in European and North American white people. There have been suggestions to adopt specific classifications of obesity in Asians (e.g. BMI 23 for overweight and 25 or 27 kg/m2 for obesity) and this will greatly affect the prevalence estimates of obesity worldwide (currently at about 250 million people). Particularly for health promotion purposes BMI may be replaced by a classification based on waist circumference, but also specific classifications for different ethnic groups may be necessary. The number of diabetics has been projected to increase from 135 million in 1995 to 300 million in 2025. Much of this increase will be seen in Asia. In summary, both obesity and type 2 diabetes are common consequences of changing lifestyles (increased sedentary lifestyles and increased energy density of diets). Both are potentially preventable through lifestyle modification on a population level, but this requires a coherent and multifaceted strategy. Such strategies are not developed or implemented. These developments point toward the great urgency to develop global and national plans for adequate prevention and management of obesity and type 2 diabetes mellitus.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".