Review on the update in obesity management: epidemiology
Bibliographic record
Abstract
Obesity remains one of the largest public health issues in the developed world. Over the past 50 years, the prevalence of this disease has risen to epidemic proportions and remains on the rise. Importantly, the incidence of obesity coincides with an increased risk of cardiovascular disease, type II diabetes, hypertension, fatty liver disease, obstructive sleep apnoea and several cancers. This article is the first of a three-part series of reviews surveying the obesity epidemic and interventions to address it. It provides an overview of the disease's prevalence, aetiology and comorbidities as well as the guidelines currently available to treat obesity. Obesity is a multifactorial disease with a complex aetiology. Genetic, environmental and epigenetic factors contribute to the occurrence of obesity. Examples include the thrifty gene hypothesis, epigenetics and the presence of obesogenic environments. Furthermore, an imbalance in energy intake versus expenditure encourages weight gain. Current guidelines aim to instruct primary care practitioners on the appropriate diagnostic and therapeutic tools to use in patients with obesity. Obesity remains an important public health concern with many causes, influences and outcomes for patients.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.029 | 0.014 |
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".