Prevention and harm reduction of obesity (clinical prevention)
Bibliographic record
Abstract
KEY MESSAGES FOR HEALTHCARE PROVIDERS • Obesity is a complex chronic disease in which abnormal or excess body fat (adiposity) impairs health, increases the risk of long-term medical complications, and reduces lifespan. • Obesity arises from a complex interplay of genetic, biological, behavioural, psychosocial and environmental factors, and can develop via slow and steady weight gain over an extended period, or from rapid bursts of weight gain. • Obesity prevention should take place in a range of settings that access whole populations or high-risk groups. The individual-based approach to prevention is primarily used by healthcare providers and targets those with the highest level of risk of obesity. The population-based approach addresses the behavioural, sociocultural and environmental factors that contribute to non-communicable diseases in populations, including obesity. • Primary care clinicians have an important role in early identification of people living with obesity. Regular assessments of body weight are needed to catch early weight gain. (See the chapters 'Assessment of people living with obesity' and 'Primary care and primary healthcare in obesity management'.) • Primary care clinicians should initiate discussion around weight gain early and contemplate interventions that consider its complex causes, providing guidance beyond 'eat less and move more'. • Many medications are associated with weight gain side-effects that can contribute to long-term weight gain. The risks and benefits of such medications should be weighed up for each specific person before prescribing. • Excess pregnancy weight gain and post-pregnancy weight retention are significantly reduced with behavioural interventions. Primary care clinicians should counsel women attending prenatal care not to exceed pregnancy weight gain guidelines, in the course of dietary, physical activity and psychological interventions during prenatal visits. • Health benefits of smoking cessation outweigh the cardiovascular consequences associated with smoking cessation-related weight gain. • Short-term behavioural interventions (generally 6 months or less) aimed at preventing weight gain during young adulthood, menopause, smoking cessation and breast cancer treatment have not yet been shown to be effective. • Longer-term interventions are likely to be needed to properly examine strategies for preventing weight gain for many of these high-risk groups and in the general population.
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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 | 0.012 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.014 | 0.018 |
| Insufficient payload (model declined to judge) | 0.118 | 0.038 |
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".