Current and emerging therapies for managing hyperphagia and obesity in Prader‐Willi syndrome: A narrative review
Bibliographic record
Abstract
In early childhood, individuals with Prader-Willi syndrome (PWS) experience excess weight gain and severe hyperphagia with food compulsivity, which often leads to early onset morbid obesity. Effective treatments for appetite suppression and weight control are currently unavailable for PWS. Our aim to further understand the pathogenesis of PWS led us to carry out a comprehensive search of the current and emerging therapies for managing hyperphagia and extreme weight gain in PWS. A literature search was performed using PubMed and the following keywords: "PWS" AND "therapy" OR "[drug name]"; reference lists, pharmaceutical websites, and the ClinicalTrials.gov registry were also reviewed. Articles presenting data from current standard treatments in PWS and also clinical trials of pharmacological agents in the pipeline were selected. Current standard treatments include dietary restriction/modifications, exercise, and growth hormone replacement, which appear to have limited efficacy for appetite and weight control in patients with PWS. The long-term safety and effectiveness of bariatric surgery in PWS remains unknown. However, many promising pharmacotherapies are in development and, if approved, will bring much needed choices into the PWS pharmacological armamentarium. With the progress that is currently being made in our understanding of PWS, an effective treatment may not be far off.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".