Targeting abdominal obesity and the metabolic syndrome to manage cardiovascular disease risk
Bibliographic record
Abstract
Almost every week there is an article published in a major national newspaper on the epidemic proportions reached by obesity in various countries. The pathophysiology of obesity is complex and some people are more prone to accumulate excess fatness and to develop related metabolic complications than others. However, despite individual differences in genetic susceptibility to body fat accumulation, it has become obvious that the epidemic proportions reached by obesity is partly the result of a pronounced reduction in the level of daily physical activity as we have engineered for ourselves a very comfortable sedentary environment. For example, most of us are now sedentary during working hours and we use transportation devices (cars, trains, planes) to go from one place to another. In addition, what was centuries ago a constant daily struggle (getting access to food, sometimes in limited quantities) has become very easy: we are surrounded by energy dense, refined foods which promote the passive overconsumption of calories. Therefore, an environment favouring minimal energy expenditure but facilitating overeating largely contributes to the worldwide epidemic of obesity. However, although it is generally perceived that obesity causes prejudice to health, this condition has, for a long time, left cardiologists rather perplexed. For example, although epidemiological studies have shown that an excess relative body weight (the most commonly used index being the body mass index (BMI) expressed in kg of body weight divided by height in m2) is associated with increased mortality and comorbidities such as hypertension, dyslipidemia and diabetes,w1–4 obesity assessed by the BMI has often failed to be identified as an independent risk factor for cardiovascular disease (CVD), once adjustments are made for the presence of comorbidities. There are two possible explanations for the weak independent association between obesity and CVD. First of all, it is possible that the …
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".