IGF-1 Assessment During Weekly Somatrogon Treatment in Pediatric Patients With GH Deficiency
Bibliographic record
Abstract
Context: In patients with GH deficiency (GHD) receiving GH treatment, IGF-1 concentrations are used by physicians to monitor treatment safety and efficacy and guide dosing decisions. Somatrogon is a long-acting GH approved as a once-weekly treatment for pediatric GHD. Somatrogon administration results in characteristic changes in the IGF-1 profile, with values measured at 96 hours postdose representing mean IGF-1 concentrations that best reflect overall somatrogon exposure. Objective: To develop a simple method to enable physicians to predict mean IGF-1 concentrations following somatrogon dosing, based on a single IGF-1 measurement taken at any point during the 7-day dosing interval. Methods: Data from phase 2 and phase 3 somatrogon studies were used to develop a 2-compartment pharmacokinetic model with delayed first-order absorption. An indirect-response pharmacokinetic/pharmacodynamic model was applied to the predicted somatrogon concentrations, and model simulations were used to predict IGF-1 and IGF-1 SD score (SDS) levels for participants in both studies. Results: A total of 16,213 dosing records (from 42 and 109 participants in the phase 2 and 3 studies, respectively) were used for the simulations, generating predicted values for IGF-1 and IGF-1 SDS. Predicted values were scaled against the respective values at 96 hours (day 4). These values were used to create a table showing the adjustments required to predict mean IGF-1 and IGF-1 SDS values depending on time after dose. Conclusion: We developed a simple method enabling physicians to predict mean weekly IGF-1 values using IGF-1 values measured at any point in the dosing interval.
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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 | 0.002 |
| 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) | 0.001 | 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".