Use of glucagon‐like peptide‐1 receptor agonists for pediatric patients with obesity and diabetes: The providers' perspectives
Bibliographic record
Abstract
BACKGROUND: Glucagon-like peptide-1 receptor agonists (GLP-1RA) have been widely used in adults with Type 2 diabetes (T2D) and obesity. We sought to evaluate the experience of pediatric endocrinology providers with GLP-1RA and factors that guide them on whether and how to prescribe these medications. METHODS: We surveyed the members of the Pediatric Endocrine Society regarding the use of GLP-1RA in their practice. RESULTS: The respondents (n = 102) were predominantly from academic centers (84%) and 75%reported using GLP-1RA in pediatric patients, mostly to treat T2D and obesity. Patient tolerance for the medication was reported to be the driving factor determining the duration of treatment. Gastrointestinal side effects were observed more commonly than local reactions or elevation of pancreatic enzymes. Lack of clinical experience was reported to be a major barrier for prescribing GLP-1RA, particularly among those with more than 5 years of clinical experience. Finally, liraglutide was used more often (93%) than other GLP-1RA. CONCLUSIONS: The use of GLP-1RA has increased in pediatric patients. Recent Food and Drug Administration approval of liraglutide for pediatric obesity will likely further increase its prescription rate. Providers should be vigilant about side effects and adjust the doses of GLP-1RA accordingly. More efforts should be made by professional societies to educate pediatric endocrinology providers about the proper use of GLP-1RA and enhance their confidence in prescribing these medications.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".