Long-Acting Growth Hormone Therapy in Pediatric Growth Hormone Deficiency: A Consensus Statement
Bibliographic record
Abstract
CONTEXT: Several long-acting growth hormone (LAGH) therapies have recently become available, but guidance on their usage in children with growth hormone (GH) deficiency is limited. METHODS: International experts in pediatric endocrinology were invited to join a consensus group based on their expertise in treating children with daily GH and LAGH. The group comprised 11 experts from 10 countries across the world. Online group meetings were held in February to March 2024 followed by a 1-day in-person meeting in May 2024 to finalize the consensus recommendations. A targeted literature search approach was used to identify and share evidence ahead of the meetings. Formulations considered were limited to those with international populations in phase III pivotal trials and regulatory approvals in multiple countries. EVIDENCE SYNTHESIS: Topics covered include patient selection and preference, dose adjustment, initiating and switching therapies, administration, adherence and missed doses, practical considerations, and knowledge gaps. LAGH formulations offer a potential advantage over daily GH injections for children with GH deficiency in terms of reduced injection frequency and treatment burden; this may also be associated with improved adherence and treatment outcomes over time. However, data on LAGH in pediatric GH deficiency are mostly limited to clinical trials, and long-term, real-world data are currently lacking. CONCLUSION: This article provides an international consensus on the use of LAGH therapy in children with GH deficiency to guide practitioners when considering these new treatment options for their patients. Long-term data are needed to fill current data gaps and allow the creation of comprehensive evidence-based recommendations.
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 | 0.058 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.011 | 0.012 |
| 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".