Bibliographic record
Abstract
© 2002 Canadian Medical Association or its licensors Obesity is now pandemic, affecting millions of peo-ple worldwide.1 In rich countries, between 10%and 20 % of people are obese, and the problem is not unknown even in poor countries.1 In the United States, the National Institutes of Health and the surgeon general have acknowledged the importance of the problem and are developing public health strategies to curb the epidemic.2,3 Obesity is a condition of excessive body fat that results from a chronic energy imbalance whereby intake exceeds expenditure. Excess body fat increases an individual’s risk of premature death from chronic diseases such as coronary heart disease, stroke, type 2 diabetes mellitus, gallbladder disease and some cancers.2 The direct medical costs attrib-utable to adult obesity in Canada are estimated to have been $1.8 billion in 1997, or 2.4 % of total direct medical costs.4 Thus, the public health burden of obesity and re-lated disorders is great. The World Health Organization recommends the con-tinued surveillance of the population prevalence of obesity using body mass index (BMI, calculated as kg/m2) as the indicator. There is recent evidence that there have been large in-creases in the national prevalence of overweight and obe-sity in Canadian children and adults over the last 2 decades, similar to the increases observed in other industrialized na-tions.5,6 Although the causes of this are not well defined, lack of physical activity may be an important factor.7 The purpose of this report is to present obesity surveil-lance maps for Canadian adults (aged ≥ 20 years) from 1985 to 1998 in an effort to describe the dramatic increases in the The Canadian obesity epidemic, 1985–1998
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".