The Use of Evidence-Based Dietary Interventions for the Management of Obesity
Bibliographic record
Abstract
Obesity has become a serious public health issue worldwide, with its prevalence steadily increasing. The potential consequences of this chronic disease, including cardiovascular disease, increased morbidity, and mortality, pose a significant burden on individuals and healthcare systems. Evidence has established that lifestyle and dietary modification are central to achieving effective weight loss. One approach shown to be efficacious in achieving weight loss is the use of a very low calorie ketogenic diet (VLCKD), which includes stages of induced ketosis, followed by a reintroduction to a low calorie diet and maintenance diet. Such regimens have been shown to result in sustained weight loss and, for some people, remission of Type 2 diabetes (T2D). A similar approach, which may also be a component of the VLCKD, is the use of total or partial replacement of meals using nutritionally complete shakes, bars, or soups. These may be combined with other weight loss measures, including bariatric surgery or medications. It is important that such programmes are delivered in a structured, medically-monitored, and supportive environment, such as laid out by Obesity Canada’s ‘5As’ programme. An ‘obesity shared medical appointment’ model is a multidisciplinary approach, whereby a patient with obesity is seen by a number of healthcare specialists, depending on their comorbidities. The patient also has the opportunity to meet with obesity specialists and engage in monthly patient support groups, all of which have been shown to be successful interventions in helping patients lead a healthier lifestyle, and gain more control over their weight. The following proceedings are based on talks given by leading obesity experts, presented at the 30th European Congress on Obesity (ECO 2023), which took place in Dublin, Republic of Ireland, in May 2023.
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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 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".