Promoting Lifestyle and Behavior Change in Overweight Children and Adolescents With Type 2 Diabetes
Bibliographic record
Abstract
Sixteen-year-old Juana is a high school sophomore who loves movies, the local pizza parlor, and salsa music. She is young but has the health risks of someone three times her age. With a BMI > 95th percentile for age, Juana has many of the features of metabolic syndrome and may be only months from displaying the symptoms of type 2 diabetes. Juana is one of 8.8 million American youth who are overweight or obese.1 The plight of Juana and her agemates has become increasingly visible in the media and in the scientific literature. Genetics has been blamed. Parents have been blamed. Schools have been blamed. Television has been blamed.2 Society has been blamed. Although we are closer to understanding the causes, we lack effective strategies for prevention and management of obesity and diabetes in children and adolescents. The road to obesity and diabetes may begin in utero. Low birth weight and exposure to maternal diabetes have been implicated.3,4 There may be genetic components to the problem.5 Certain genes associated with type 2 diabetes have been identified in Cree-Ojibway aboriginal children in Canada.6 Largely, however, the rise in childhood obesity corresponds to various environmental changes. Children today are less physically active than in the past. They play less outside and have less physical education in school. In high school, enrollment in daily physical education (P.E.) classes dropped from 42% in 1991 to 25% in 1995.7 Only 19% of all high school students are physically active for at least 20 minutes in P.E. classes every day during the school week. What has replaced physical activity is television watching, Internet surfing, and computer gaming. There is a strong correlation between childhood obesity and access to television. Children are more likely to be obese if they …
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".