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Record W4389300450 · doi:10.1080/17446651.2023.2290495

Somatrogon injection for the treatment of pediatric growth hormone deficiency with comparison to other LAGH products

2023· article· en· W4389300450 on OpenAlexaboutno aff
Eric Velazquez, Bradley S. Miller, Kevin C.J. Yuen

Bibliographic record

VenueExpert Review of Endocrinology & Metabolism · 2023
Typearticle
Languageen
FieldMedicine
TopicGrowth Hormone and Insulin-like Growth Factors
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineGrowth hormone deficiencyGrowth hormoneEndocrinologyHormonePediatricsInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Somatrogon (NGENLA™) is a long-acting GH (LAGH) formulation that was approved in Canada in October 2021 for the treatment of pediatric growth hormone deficiency (GHD). Somatrogon has also received approval in Australia, Japan, the European Union, the USA, and the UK. Somatrogon is a glycoprotein that utilizes three copies of the C-terminal peptide of human chorionic gonadotropin to delay its clearance allowing for once-weekly administration. AREAS COVERED: The purpose of this article is to describe the development of somatrogon for treatment of individuals with GHD. Trials of somatrogon demonstrated positive efficacy results in adults (Phase 2) and children (Phase 2 and 3) with GHD including non-inferiority of height velocity compared to daily GH, with no concerning side effects. Growth responses, pharmacodynamics and safety data are compared to other LAGH products, lonapegsomatropin and somapacitan, in Phase 3 trials in pediatric GHD. EXPERT OPINION: New LAGH products, including somatrogon, have the potential to increase patient adherence as well as improve quality of life and clinical outcomes. Clinicians will need to identify the best candidates for LAGH therapy and understand how to safely monitor and adjust therapy. Long-term surveillance studies are necessary to demonstrate adherence, efficacy, cost-effectiveness, and safety of LAGH preparations.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.367
Threshold uncertainty score0.668

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.038
GPT teacher head0.334
Teacher spread0.296 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2023
Admission routes1
Has abstractyes

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