Impact of anorectic drugs fenfluramine, dexfenfluramine, lorcaserin, and rimonabant on obesity
Bibliographic record
Abstract
Anorectic drugs are used to suppress appetite and increase satiety, thereby encouraging weight loss. Among the anorectic drugs that have been most commonly used are fenfluramine, dexfenfluramine, lorcaserin, and rimonabant, which promote weight loss in different ways and to different degrees. By reviewing recent literature, this paper comparatively assesses the benefits and risks of these anorectic drugs. Fenfluramine and dexfenfluramine, while highly effective in promoting weight loss, were associated with the highest risk of potentially fatal cardiac valve damage. Lorcaserin has proven to be significantly safer than fenfluramine and dexfenfluramine, but its impacts on weight loss were generally modest and it was less sucessful in promoting long-term weight loss. Rimonabant resulted in a more significant decrease in weight than did lorcaserin, but was associated with unpredictable psychological side effects. This review identifies lorcaserin as the safest of the anorectic drugs discussed, even if it is not the most effective in promoting and sustaining weight loss. While anorectic drugs may help to treat obesity, they are not without potentially harmful side effects and are best used in conjunction with dietary and lifestyle modifications. Future research should seek to improve the specificity of anorectic drugs and thereby reduce potential side effects.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".