Bibliographic record
Abstract
CONTEXT: Obesity has emerged as a global public health challenge. The objective of this review was to examine epidemiological aspects of obesity in the Western Hemisphere. EVIDENCE ACQUISITION: Using PubMed, we searched for publications about obesity (prevalence, trends, correlates, economic costs) in countries in North America, Central America, South America, and the Caribbean. To the extent possible, we focused on studies that were primarily population based in design and on four countries in the Western Hemisphere: Brazil, Canada, Mexico, and the United States. EVIDENCE SYNTHESIS: Data compiled by the International Obesity Task Force show a substantial level of obesity in all of or selected areas of the Bahamas, Barbados, Canada, Chile, Guyana, Mexico, Panama, Paraguay, Peru, St. Lucia, Trinidad and Tobago, the United States, and Venezuela. Furthermore, countries such as Brazil, Canada, Mexico, and the United States have experienced increases in the prevalence of obesity. In many countries, the prevalence of obesity is higher among women than men and in urban areas than in rural areas. The relationship between socioeconomic status and obesity depends on the stage of economic transition. Early in the transition, the prevalence of obesity is positively related to income whereas at some point during the transition the prevalence becomes inversely related to income. CONCLUSIONS: Like other countries in the Western Hemisphere, the four countries that we focused on have experienced a rising tide of obesity. The high and increasing prevalence of obesity and its attendant comorbidities are likely to pose a serious challenge to the public health and medical care systems in these countries.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".