Treatment of obesity: need to focus on high risk abdominally obese patients
Bibliographic record
Abstract
Editorial by Little and Byrne It is generally accepted that obesity is a health hazard because of its association with numerous metabolic complications such as dyslipidaemia, type 2 diabetes, and cardiovascular diseases.1 On that basis, health agencies 2 3 have proposed that obesity should be defined on the basis of weight in kg expressed over height in m2, the so called body mass index,4 initially described by Quetelet in 1869 (table). Epidemiological studies have reported a progressive increase in the incidence of chronic diseases such as hypertension, diabetes, and coronary heart disease with increasing body mass index.1-3 However, despite this well documented evidence, physicians are, in their daily practice, perplexed by the remarkable heterogeneity found in their obese patients. For instance, some patients show a relatively “normal” profile of metabolic risk factors despite the presence of substantial excess body fat, whereas others who are only moderately overweight can nevertheless be characterised by a whole cluster of metabolic complications, increasing the risk of type 2 diabetes, coronary atherosclerosis, and cardiovascular disease. View this table: Classification of obesity based on body mass index (BMI)2 3 #### Summary points A simple measurement such as waist circumference can indicate accumulation of abdominal fat Viscerally obese men are characterised by an atherogenic plasma lipoprotein profile A triad of non-traditional markers for coronary heart disease found in viscerally obese middle aged men (hyperinsulinaemia, raised apolipoprotein B concentration, and small LDL particles) increases the risk of coronary heart disease 20-fold Four out of five middle aged men with a waist measurement ≥90 cm and triglyceride concentrations ≥2 mmol/l are characterised by this triad Even in the absence of hypercholesterolaemia, hyperglycaemia, or hypertension, obese patients could be at high risk of coronary heart disease if they have this “hypertriglyceridaemic waist” phenotype In this regard, epidemiological and metabolic studies …
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.014 | 0.023 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".