Evidence map›Paper›PMID 40634928›Full record

ArticleBMC endocrine disorders2025

Construction and evaluation of a height prediction model for children with growth disorders treated with recombinant human growth hormone.

Feng Zhu, Anle Wu, Lingling Chen, Ya Xia, Xiaoju Luo, Jieqian Zhu, Lina Huang, Yu Zhang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC endocrine disorders, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Feng ZhuDepartment of Child Healthcare, Wenzhou People's Hospital/Wenzhou Maternal and Child Health Care Hospital, The Third Clinical Institute Affiliated to Wenzhou Medical University, The Third Affiliated Hospital of Shanghai University, Wenzhou, 325000, Zhejiang, China.
Anle WuDepartment of Child Healthcare, Wenzhou People's Hospital/Wenzhou Maternal and Child Health Care Hospital, The Third Clinical Institute Affiliated to Wenzhou Medical University, The Third Affiliated Hospital of Shanghai University, Wenzhou, 325000, Zhejiang, China.
Lingling ChenDepartment of Child Healthcare, Wenzhou People's Hospital/Wenzhou Maternal and Child Health Care Hospital, The Third Clinical Institute Affiliated to Wenzhou Medical University, The Third Affiliated Hospital of Shanghai University, Wenzhou, 325000, Zhejiang, China.
Ya XiaDepartment of Child Healthcare, Wenzhou People's Hospital/Wenzhou Maternal and Child Health Care Hospital, The Third Clinical Institute Affiliated to Wenzhou Medical University, The Third Affiliated Hospital of Shanghai University, Wenzhou, 325000, Zhejiang, China.
Xiaoju LuoDepartment of Science and Education, Wenzhou People's Hospital/Wenzhou Maternal and Child Health Care Hospital, The Third Clinical Institute Affiliated to Wenzhou Medical University, The Third Affiliated Hospital of Shanghai University, Wenzhou, 325000, Zhejiang, China.
Jieqian ZhuDepartment of Child Healthcare, Wenzhou People's Hospital/Wenzhou Maternal and Child Health Care Hospital, The Third Clinical Institute Affiliated to Wenzhou Medical University, The Third Affiliated Hospital of Shanghai University, Wenzhou, 325000, Zhejiang, China.
Lina HuangDepartment of Child Healthcare, Wenzhou People's Hospital/Wenzhou Maternal and Child Health Care Hospital, The Third Clinical Institute Affiliated to Wenzhou Medical University, The Third Affiliated Hospital of Shanghai University, Wenzhou, 325000, Zhejiang, China.
Yu ZhangDepartment of Child Healthcare, Wenzhou People's Hospital/Wenzhou Maternal and Child Health Care Hospital, The Third Clinical Institute Affiliated to Wenzhou Medical University, The Third Affiliated Hospital of Shanghai University, Wenzhou, 325000, Zhejiang, China. 1966008089@qq.com.

Funding

Wenzhou Municipal Science and Technology Bureau Project Y2023002
6 · The paper itself

Abstract

backgroundHeight gain in children with growth disorders undergoing recombinant human growth hormone (rhGH) therapy shows considerable variability. Predicting treatment outcomes is essential for optimizing individualized treatment strategies.

objectiveTo develop and evaluate a predictive model using clinical data to assess early height growth response in children with growth disorders undergoing rhGH therapy.

methodsA total of 786 children were included, randomly split into a derivation cohort (N = 551) and a test cohort (N = 235). Multiple machine learning models were built in the derivation cohort, including logistic regression, decision tree, random forest, XGBoost, LightGBM, and multilayer perceptron (MLP). Model performance was evaluated in the test cohort using area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), and accuracy metrics. Input variables included chronological age, height standard deviation score (HSDS), body mass index standard deviation score (BSDS), IGF-1, and the difference between bone age and chronological age (BA-CA).

resultsThe random forest and MLP models performed best. The random forest model achieved an AUROC of 0.9114 and an AUPRC of 0.8825. The MLP model showed accuracy, precision, specificity, and F1 scores of 0.8468, 0.8208, 0.8583, and 0.8246, respectively. Chronological age, BA-CA, HSDS, and BSDS were the most influential variables. The decision tree identified HSDS ≥ -0.72 as the primary split point.

conclusionMachine learning models, especially random forest and MLP, predict height gain effectively in children receiving rhGH therapy, aiding personalized treatment. Despite MLP's strong performance, its "black-box" nature may limit clinical adoption. Future work should focus on enhancing model interpretability.

Indexed as

Body HeightGrowth DisordersHuman Growth HormoneAdolescentChildChild, PreschoolFemaleFollow-Up StudiesHumansMachine LearningMalePrognosisRecombinant ProteinsHuman Growth HormoneRecombinant ProteinsGrowth disordersMachine learningPredictive modelRecombinant human growth hormoneShort-term response

Identifiers

PMID40634928
PMCPMC12239477

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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 Socratic graph.