Bibliographic record
Abstract
BACKGROUND: Obesity is a chronic and heterogeneous medical condition. Weight loss is clearly the most desirable goal in obese subjects management. Successful obesity treatment should be defined as long-term weight loss maintenance. In this chapter, we briefly review the current evidences regarding the treatment of obesity. METHODS: We searched MEDLINE and PubMed for original articles published between 1995 and 2006, focusing on obesity treatment. The search terms we used, alone or in combination, were 'obesity', 'lifestyle changes', 'diet', 'exercise', 'pharmacological treatment', 'surgical treatment'. CONCLUSIONS: The conventional management of obese patients involves weight reduction with lifestyle changes, including dietary therapy and increased physical activity, or a combined approach with lifestyle changes and pharmacological or surgical interventions. Exercise appears crucial in the successful maintenance of weight loss and in fostering cardiovascular health in obese patients. Some anti-obesity drugs, such as sibutramine and orlistat, have been shown to induce a significant weight loss and long-term weight loss maintenance. Surgical therapy is often necessary in morbidly obese patients and generally results in more significant and long-lasting weight loss than other treatments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".