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Model predicts growth hormone therapy responses in children with short stature (opens in a new tab)

medicalxpress.com · 2026-10-05

Short answerEvidenceSource

Short answer

Mostly not supported

Mostly not supported.

The claims we could check match the study, but some claims were not covered by the evidence reviewed.

  • 1 supported
  • 8 not covered

Checked against the study summary. The full text wasn't available, so some details couldn't be settled either way.

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Follow the evidence trail
1
2

NewsLink checks it

Mostly not supported

The one claim we could check holds up. One of nine claims matches the study. This overall rating is based only on the claims we could check. Eight claims the study doesn't address.

  • 1 supported
  • 8 not covered
Open claim evidence
3
Then inspect each claim

Evidence layer

Claim by claim

Each claim gets a verdict. Expand it to see the evidence directly below.

9 claims in this story

Showing all 9 claimsChoose a verdict to focus the list.

Claim 1 of 9Not coveredIdiopathic short stature is a condition in which children are much shorter than expected for age and sex without an identifiable medical cause, and growth hormone therapy is often used but responses vary widely.View evidenceHide evidence

Why this verdict

The abstract-level profile supports that the study concerns prepubertal children with idiopathic short stature receiving GH and is motivated by individualized response prediction. However, the supplied abstract profile does not verify the full condition definition, that GH therapy is 'often used,' or the broad statement that responses vary widely.

1

Study evidence

A Gompertz-based NLME model characterized the longitudinal dose–response of GH on height percentile in 91 prepubertal ISS patients, estimating a baseline height percentile of 0.63 ± 0.90 and an upper growth limit of 17.2 ± 11.1.baseline 0.63 ± 0.90; upper growth limit 17.2 ± 11.1

“A total of 91 prepubertal patients with ISS were included in this study.”
2

Study evidence

Body mass index (BMI), insulin-like growth factor–binding protein 3 (IGFBP-3), and paternal height were reported as significant baseline covariates influencing total growth in the NLME model.

“stepwise covariate modeling identified significant baseline predictors.”
Claim 2 of 9Not coveredResearchers led by professor Jung-woo Chae studied changes in height percentile over time in children with idiopathic short stature receiving growth hormone treatment.View evidenceHide evidence

Why this verdict

The scientific content that the study modeled height percentile over time in children with ISS receiving GH is supported. However, the profile does not provide author leadership details such as 'led by professor Jung-woo Chae,' so the claim as stated is not fully verifiable at abstract depth.

1

Study evidence

A Gompertz-based NLME model characterized the longitudinal dose–response of GH on height percentile in 91 prepubertal ISS patients, estimating a baseline height percentile of 0.63 ± 0.90 and an upper growth limit of 17.2 ± 11.1.baseline 0.63 ± 0.90; upper growth limit 17.2 ± 11.1

“A total of 91 prepubertal patients with ISS were included in this study.”
Claim 3 of 9Not coveredThe study was a retrospective, single-center analysis of 91 prepubertal Korean children with idiopathic short stature who received recombinant human growth hormone between July 2020 and December 2023.View evidenceHide evidence

As stated91 children

Why this verdict

The profile supports inclusion of 91 prepubertal patients with ISS and GH exposure. It does not verify several specific story details: retrospective single-center design, Korean cohort, recombinant GH wording, or the July 2020–December 2023 treatment window.

1

Study evidence

A Gompertz-based NLME model characterized the longitudinal dose–response of GH on height percentile in 91 prepubertal ISS patients, estimating a baseline height percentile of 0.63 ± 0.90 and an upper growth limit of 17.2 ± 11.1.baseline 0.63 ± 0.90; upper growth limit 17.2 ± 11.1

“A total of 91 prepubertal patients with ISS were included in this study.”
Claim 4 of 9Not coveredOver a mean treatment period of 619 ± 307 days, the mean height percentile increased from 1.26 at baseline to 9.16 at follow-up, and the model estimated a total growth-response parameter of about 17.2 percentile points.View evidenceHide evidence

As statedmean height percentile 1.26 to 9.16; ~17.2 percentile points

Why this verdict

The profile supports an upper growth limit estimate of 17.2 ± 11.1, broadly consistent with the story's approximately 17.2 percentile-point model parameter. But the abstract profile does not report the mean treatment duration of 619 ± 307 days or the observed mean height percentile change from 1.26 to 9.16; it reports a model baseline parameter of 0.63 ± 0.90 instead. Those stated observed values are therefore not verifiable at abstract depth.

1

Study evidence

A Gompertz-based NLME model characterized the longitudinal dose–response of GH on height percentile in 91 prepubertal ISS patients, estimating a baseline height percentile of 0.63 ± 0.90 and an upper growth limit of 17.2 ± 11.1.baseline 0.63 ± 0.90; upper growth limit 17.2 ± 11.1

“A total of 91 prepubertal patients with ISS were included in this study.”
Claim 5 of 9Not coveredThree baseline characteristics were associated with greater predicted response: higher BMI, lower IGFBP-3, and shorter paternal height, with BMI described as the strongest association.View evidenceHide evidence

Why this verdict

The profile supports that BMI, IGFBP-3, and paternal height were significant covariates influencing total growth. However, the supplied abstract profile does not give the direction of the covariate effects, effect magnitudes, or evidence that BMI was the strongest association.

1

Study evidence

Body mass index (BMI), insulin-like growth factor–binding protein 3 (IGFBP-3), and paternal height were reported as significant baseline covariates influencing total growth in the NLME model.

“stepwise covariate modeling identified significant baseline predictors.”
Claim 6 of 9Not coveredTwo-year simulations suggested predicted outcomes differed by patient profile and GH exposure, with median predicted height percentiles ranging from 4.0% in lower-response profiles to 31.0% in higher-response profiles.View evidenceHide evidence

As stated4.0% to 31.0%

Why this verdict

The profile supports individualized prediction using the fitted model and web tool, but it does not report two-year simulation scenarios, patient-profile strata, GH-exposure comparisons, or median predicted height percentiles ranging from 4.0% to 31.0%.

1

Study evidence

A Gompertz-based NLME model characterized the longitudinal dose–response of GH on height percentile in 91 prepubertal ISS patients, estimating a baseline height percentile of 0.63 ± 0.90 and an upper growth limit of 17.2 ± 11.1.baseline 0.63 ± 0.90; upper growth limit 17.2 ± 11.1

“A total of 91 prepubertal patients with ISS were included in this study.”
2

Study evidence

The developed model was integrated into a web-based tool, allowing clinicians to predict individualized growth trajectories and optimize GH treatment strategies.

“The developed model was integrated into a web-based tool, allowing clinicians to predict individualized growth trajectories and optimize GH treatment strategies”
Claim 7 of 9Not coveredThe team developed GrowCast, a web-based tool that generates individualized predicted height and percentile trajectories using age, sex, height, weight, paternal height, IGFBP-3 level, and GH dose.View evidenceHide evidence

Why this verdict

The profile supports that the finalized model was integrated into a web-based tool for individualized growth-trajectory prediction and GH treatment optimization. It does not verify the tool name 'GrowCast' or the full stated input list of age, sex, height, weight, paternal height, IGFBP-3 level, and GH dose.

1

Study evidence

The developed model was integrated into a web-based tool, allowing clinicians to predict individualized growth trajectories and optimize GH treatment strategies.

“The developed model was integrated into a web-based tool, allowing clinicians to predict individualized growth trajectories and optimize GH treatment strategies”
2

Study evidence

Body mass index (BMI), insulin-like growth factor–binding protein 3 (IGFBP-3), and paternal height were reported as significant baseline covariates influencing total growth in the NLME model.

“stepwise covariate modeling identified significant baseline predictors.”
Claim 8 of 9Not coveredThe article says GrowCast may help clinicians discuss treatment scenarios, but it is not yet a validated dosing-prescription tool and larger prospective multicenter studies are needed before routine clinical use.View evidenceHide evidence

Why this verdict

The profile supports that the tool was intended for clinician-facing individualized prediction and notes that the abstract lacks validation details. However, the supplied abstract profile does not verify that the paper specifically says the tool is not a validated dosing-prescription tool or that larger prospective multicenter studies are required before routine clinical use.

1

Study evidence

The developed model was integrated into a web-based tool, allowing clinicians to predict individualized growth trajectories and optimize GH treatment strategies.

“The developed model was integrated into a web-based tool, allowing clinicians to predict individualized growth trajectories and optimize GH treatment strategies”
Claim 9 of 9SupportedThe researchers used a Gompertz nonlinear mixed-effects model with cumulative growth hormone exposure to characterize growth trajectories and individual treatment response.View evidenceHide evidence

Why this verdict

The profile states that a Gompertz-based nonlinear mixed-effects model was fitted to longitudinal age- and sex-adjusted height percentile data, using cumulative GH dose as the exposure metric, to describe the GH dose-response relationship.

1

Study evidence

A Gompertz-based NLME model characterized the longitudinal dose–response of GH on height percentile in 91 prepubertal ISS patients, estimating a baseline height percentile of 0.63 ± 0.90 and an upper growth limit of 17.2 ± 11.1.baseline 0.63 ± 0.90; upper growth limit 17.2 ± 11.1

“A total of 91 prepubertal patients with ISS were included in this study.”
Then look for missing context

Context layer

What the story left out

Important study details the story did not include.

  • Model adequacy/performance was assessed using visual predictive checks and goodness-of-fit plots.

    The story presentation does not mention the reported VPC or goodness-of-fit diagnostic assessment. This is material because model credibility underpins the prediction tool, and the abstract profile also notes that no numeric diagnostic results are provided.

    From secondary_data: NLME model diagnostics (VPC, GOF)

  • Covariate-effect limitation: the abstract reports covariate significance but does not provide effect sizes, coefficients, p-values, confidence intervals, or selection criteria details.

    The story reports directional and comparative covariate claims without noting that the abstract-level evidence lacks effect magnitudes and statistical details. This omission matters because it affects how strongly readers should interpret the predictor findings.

    From Secondary-data NLME with stepwise covariate selection

4 things the story did carry across
  • Core study model: a Gompertz-based NLME longitudinal dose-response model of GH effects on age- and sex-adjusted height percentile in 91 prepubertal ISS patients using cumulative GH dose exposure.
  • Covariate modeling identified BMI, IGFBP-3, and paternal height as significant baseline covariates influencing total growth in the model.
  • The finalized model was integrated into a web-based tool for individualized growth-trajectory prediction and GH treatment optimization.
  • Important validation limitation: the abstract profile does not report external validation, prospective evaluation, or independent testing of the web-based tool/model.
Then read the study layer

Study layer

Study at a glance

Scan the study first. Expand only the parts you want to inspect.

Pieces of work

4

Evidence read

study summary

Lead result

secondary data

1Lead resultsecondary dataDevelop and fit a Gompertz-based nonlinear mixed-effects (NLME) longitudinal dose–response model to quantify growth hormone (GH) treatment effects on height percentile trajectories in prepubertal children with idiopathic short stature (ISS).Gompertz-based NLME longitudinal dose–response modelExpand

In plain English

A Gompertz-based nonlinear mixed-effects (NLME) longitudinal dose–response model was developed and fitted to repeated measures of height percentile (age- and sex-adjusted) from 91 prepubertal children with idiopathic short stature (ISS), using cumulative growth hormone (GH) dose (mg/kg) as the exposure metric. Stepwise covariate modeling identified body mass index (BMI), insulin-like growth factor–binding protein 3 (IGFBP3), and paternal height as significant predictors of total growth. Model performance was assessed with visual predictive checks and goodness-of-fit plots. The final model reported a baseline height percentile of 0.63 ± 0.90 and an upper growth limit of 17.2 ± 11.1 and was implemented in a web-based tool for individualized prediction.

Key findings

  • A Gompertz-based NLME model characterized the longitudinal dose–response of GH on height percentile in 91 prepubertal ISS patients, estimating a baseline height percentile of 0.63 ± 0.90 and an upper growth limit of 17.2 ± 11.1.baseline 0.63 ± 0.90; upper growth limit 17.2 ± 11.1
  • Body mass index (BMI), insulin-like growth factor–binding protein 3 (IGFBP3), and paternal height were identified as significant covariates influencing total growth in the model.
“A total of 91 prepubertal patients with ISS were included in this study.”
What this piece can’t prove
  • Abstract does not report duration of follow-up, timing/number of longitudinal measurements, or estimation algorithm/software.

3 further details could not be confirmed from the summary.

2secondary dataIdentify baseline predictors/covariates (eg, BMI, IGFBP-3, paternal height) that explain inter-individual variability in growth response within the NLME framework to support personalized dosing/response prediction.Secondary-data NLME with stepwise covariate selectionExpand

In plain English

Within a Gompertz-based nonlinear mixed-effects model of height-percentile response to cumulative GH dose in 91 prepubertal children with idiopathic short stature, stepwise covariate modeling identified body mass index (BMI), insulin-like growth factor–binding protein 3 (IGFBP-3), and paternal height as significant baseline predictors of total growth.

Key findings

  • Body mass index (BMI), insulin-like growth factor–binding protein 3 (IGFBP-3), and paternal height were reported as significant baseline covariates influencing total growth in the NLME model.
“stepwise covariate modeling identified significant baseline predictors.”
What this piece can’t prove

2 further details could not be confirmed from the summary.

3secondary dataAssess model adequacy/performance (eg, visual predictive checks and goodness-of-fit) for the finalized NLME model.secondary data: NLME model diagnostics (VPC, GOF)Expand

In plain English

The abstract reports that the finalized Gompertz-based NLME model was evaluated using visual predictive checks (VPCs) and goodness-of-fit (GOF) plots to assess model performance/adequacy, but it does not provide numeric results or diagnostic conclusions in the abstract.

Key findings

  • The study reports that visual predictive checks and goodness-of-fit plots were used to assess the finalized NLME model's performance/adequacy.
“The model performance was assessed using visual predictive checks and goodness-of-fit plots.”
What this piece can’t prove
  • Assessment is based solely on the abstract, which only states the use of VPCs and GOF plots without reporting diagnostic outcomes or implementation details.

2 further details could not be confirmed from the summary.

4otherTranslate the finalized predictive model into a web-based clinical tool for individualized growth-trajectory prediction and treatment optimization.Expand

In plain English

The authors report that the finalized NLME model was integrated into a web-based tool intended to allow clinicians to predict individualized growth trajectories and optimize GH treatment strategies for children with idiopathic short stature; the abstract does not provide implementation, availability, or validation details.

Key findings

  • The developed model was integrated into a web-based tool, allowing clinicians to predict individualized growth trajectories and optimize GH treatment strategies.
“The developed model was integrated into a web-based tool, allowing clinicians to predict individualized growth trajectories and optimize GH treatment strategies”
What this piece can’t prove

3 further details could not be confirmed from the summary.

Finally, the search trail

Method layer

NewsLink found the paper. Tessa takes you deeper.

NewsLink checks the story. Tessa is where you inspect the paper, authors, evidence, and research context.

Open the paper in Tessa

Longitudinal Modeling and Simulation of Growth Hormone Efficacy in Children With Idiopathic Short Stature

Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research · 2026

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Why this one

Near certain

NewsLink found the paper. Tessa is where you inspect it deeply.

Papers considered

The selected paper, plus nearby candidates.

PubMed, Europe PMC, Crossref · 29 candidate papers

Selected

Longitudinal Modeling and Simulation of Growth Hormone Efficacy in Children With Idiopathic Short Stature

Value in Health : the Journal of the International Society for Pharmacoeconomics and Outcomes Research · 2026 · PubMed, Europe PMC, Crossref

Tessa (opens in a new tab)Source (opens in a new tab)
Candidate

Unintended Consequences of Expanded Magnetic Resonance Imaging Reimbursement: A Nationwide Analysis Revealing Low Clinical Efficiency.

Value in Health : the Journal of the International Society for Pharmacoeconomics and Outcomes Research · 2026 · PubMed

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And 23 more candidates considered.