Challenges and New Directions in Obesity Management: Lifestyle Modification Programmes, Pharmacotherapy and Bariatric Surgery
Bibliographic record
Abstract
Obesity is a growing health problem worldwide, and it is associated with serious medical and psychosocial comorbidities, impairment of health-related quality of life (HRQoL) and an increased risk of mortality. This article aims to discuss challenges faced by health-care providers when managing patients with obesity and to highlight sustainable policies in clinical practice and future research. All health professionals dealing with obesity should consider lifestyle-modification programmes within a multidisciplinary setting as the key element of weight management. However, standardisation is needed in terms of nature, content and duration of these programmes in order to facilitate their implementation in clinical practice at different levels. Moreover, health professionals should be aware that these programmes, for patients indicating “non-response,” can be combined with recently approved anti-obesity drugs such as liraglutide, naltrexone/bupropion, lorcaserin and phentermine/topiramate, as well as with relatively less invasive bariatric surgery techniques such as Lap Band, endoscopic sleeve gastroplasty and gastric bypass. In any case, neither anti-obesity medication nor bariatric surgery should be considered as a miracle treatment in itself. At the same time, the field of obesity is still lacking in literature on some hot topics that need further investigation, including (i) a new phenotype termed sarcopenic obesity, to clarify its definition, potential health consequences and eventual treatment if necessary; (ii) issues that go beyond body weight, for instance, HRQoL that has been poorly studied in some populations affected by obesity; and (iii) the long-term effect of sleeve gastrectomy technique, which is becoming the most commonly used bariatric surgical procedure, perhaps to be studied using long-term randomised controlled trials that guarantee completeness of follow-up, in order to avoid misunderstanding and bias in interpretation of results.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".