EFETIVIDADE DOS SUPRESSORES DE APETITE NO TRATAMENTO DA OBESIDADE: UMA REVISÃO SISTEMÁTICA
Bibliographic record
Abstract
Obesity is a multifactorial condition that requires integrated treatments, including anorectic drugs as a complement to lifestyle changes, which act on the central nervous and endocrine systems, promoting satiety and weight loss. However, inappropriate use can cause adverse effects and dependence, highlighting the need for the present study. This study aimed to analyze the effects and adverse reactions of the main anorectic drugs. Specifically, we sought to characterize their classes and describe the side effects associated with their isolated or combined use. A systematic review was carried out covering 3,905 articles identified in databases such as PubMed, SciELO, Cochrane Library, and other platforms. After applying criteria based on the PICOS model, 625 studies were considered eligible, and five observational studies, published between 2014 and 2024, were analyzed in detail. Methodological quality was assessed by the Newcastle-Ottawa scale. The five studies analyzed approximately 691 thousand participants from different countries. Liraglutide was the most effective in weight loss, with an average of -7.7 kg, while phentermine-topiramate stood out in weight reduction, with more than 40% of cases recording a loss of more than 5% of body weight. However, adverse effects such as nausea, insomnia, and cardiovascular risks were reported. The efficacy of the drugs varies between classes, with liraglutide and phentermine-topiramate being the most promising. The combination of pharmacological interventions with lifestyle changes proved to be more effective and safe, reinforcing the importance of medical supervision. Anorectics are valuable tools in the management of obesity, as long as they are used under medical supervision and associated with healthy habits. Personalized strategies and future studies are essential to ensure long-term safety and efficacy.
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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 | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".