ArticleEndocrine connections2018
Pre-treatment growth and IGF-I deficiency as main predictors of response to growth hormone therapy in neural models.
Article in Endocrine connections, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Nutritional Status of Children with Short Stature Is Oppositely Associated with Growth Hormone Peak in Stimulation Tests and Insulin-like Growth Factor-1 Concentration.Journal of clinical medicine · 2026Article
- Predictors of Response to Growth Hormone Therapy in Children with Idiopathic Growth Hormone Deficiency: A Narrative Review.Children (Basel, Switzerland) · 2026Review
- Extending curve matching with flexible hyperparameter selection to predict response to long-acting PEGylated growth hormone treatment in growth hormone deficiency children: method development and validation.Journal of endocrinological investigation · 2025Article
- Metabolic factors influencing the efficacy of recombinant human growth hormone therapy in children with short stature.Frontiers in endocrinology · 2025Article
- Transient Isolated, Idiopathic Growth Hormone Deficiency-A Self-Limiting Pediatric Disease with Male Predominance or a Diagnosis Based on Uncertain Criteria? Lesson from 20 Years' Real-World Experience with Retesting at One Center.International journal of molecular sciences · 2024Article
- Isolated childhood growth hormone deficiency: a 30-year experience on final height and a new prediction model.Journal of endocrinological investigation · 2022Article
- Curve matching to predict growth in patients receiving growth hormone therapy: An interpretable & explainable method.Frontiers in endocrinology · 2022Observational
- Important Tools for Use by Pediatric Endocrinologists in the Assessment of Short StatureJournal of clinical research in pediatric endocrinology · 2021Review
- An eHealth Framework for Managing Pediatric Growth Disorders and Growth Hormone Therapy.Journal of medical Internet research · 2021Article
- Significance of Direct Confirmation of Growth Hormone Insensitivity for the Diagnosis of Primary IGF-I Deficiency.Journal of clinical medicine · 2020Article
- A systematic review on machine learning in sellar region diseases: quality and reporting items.Endocrine connections · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mathematical models have been applied in prediction of growth hormone treatment effectiveness in children since the end of 1990s. Usually they were multiple linear regression models; however, there are also examples derived by empirical non-linear methods. Proposed solution consists in application of machine learning technique - artificial neural networks - to analyse this problem. This new methodology, contrary to previous ones, allows detection of both linear and non-linear dependencies without assuming their character
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.