Bibliographic record
Abstract
Studies have identified major risk factors for cardiovascular disease (CVD). Some are non-modifiable but many reflect lifestyle habits, such as a deleterious diet and lack of physical activity/exercise, which lead to overweight and obesity.1 2 Obesity has reached epidemic proportions in the United States as well as much of the industrialised world, and is increasing in the developing world.3–6 In the most widely used classification of body mass, body weight is expressed as a body mass index (BMI).3 In adults, obesity is defined as a BMI ⩾30 kg/m2 which is further subdivided into grades (box 1). Obesity has also been defined in children.4 P Poirier The most rapidly growing segment of the obese adult population is that comprising people who are severely obese. Between 1986 and 2000, people with a BMI >30, 40 and 50 kg/m2, are reported to have doubled, quadrupled and quintupled, respectively, in the United States.7 Data indicate that the prevalence of extreme obesity in children is increasing.4 It has been suggested that the life-shortening effect of obesity could increase as the obese who are now at younger ages carry their increased risk of death into middle and older ages.8 The estimated years of life lost due to obesity differ among races and gender and it was estimated that the optimal BMI for adults aged 18–85 years is 23–25 kg/m2 for white subjects and 23–30 kg/m2 for black subjects.9 However, obesity is a remarkably heterogeneous condition.10 11 Obesity as defined by the BMI is undoubtedly associated with an increased rate of comorbidities and cardiovascular mortality10 12 and obese people considered “at risk” are mostly characterised by features associated with metabolic disturbances.12 13 The epidemic of obesity is occurring on …
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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 | 0.007 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.054 | 0.018 |
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".