How the COVID-19 pandemic has affected obesity levels and how liraglutide may play a role in its control
Bibliographic record
Abstract
In March 2020, the UK Government introduced formal social lockdown measures to restrict the spread of the COVID-19 virus. Both the lockdowns and the pandemic have had considerable social and health consequences beyond the direct death toll attributable to COVID-19. One of the secondary effects of COVID-19 and lockdown is increased levels of obesity. The most common and convenient measurement of obesity that is currently used is body mass index (BMI), with a BMI over 30 being classed as obese. Excess weight is one of the few modifiable factors for COVID-19 and, as such, achieving a healthier weight is crucial to keep the nation fit and well as we move forward. There are many ways in which weight may be controlled or managed, from exercise, diet and nutrition, to surgery to medication. Liraglutide is the drug that is commonly known as Saxenda®. It may be prescribed for individuals with a BMI of 30kg/m2 or more or those with a BMI of 27kg/m2 who have another weight-related illness, such as high blood pressure, type 2 diabetes or dyslipidemia. It has a number of cautions and contraindications, and the side effects experienced are generally gastrointestinal-related. As with many weight management programmes, Saxenda works in conjunction with a reduced calorie diet and an increase in physical activity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".