Evidence map›Paper›PMID 41207960›Full record

ArticleEuropean spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society2026

Prediction of Cobb angle progression in female patients with mild-to-moderate adolescent idiopathic scoliosis.

Satoshi Kubota, Kengo Kawaguchi, Haruhisa Yanagida, Toru Yamaguchi

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Article in European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

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2citing papers in PubMed, 1 pooled it
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1 · What the graph read from it

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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

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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 synthesis or guideline pooled it.

  1. Pooled it
  2. Radiographic and systemic predictors of persistent low back pain following total hip arthroplasty: roles of relative pelvic version and osteosarcopenia.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026
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5 · Who and what money

Authors and funding

4 authors.

Satoshi KubotaDepartment of Orthopaedic and Spine Surgery, Fukuoka Children's Hospital, Fukuoka, Japan.
Kengo KawaguchiDepartment of Orthopaedic Surgery, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan. kawaguchi.kengo.934@m.kyushu-u.ac.jp.
Haruhisa YanagidaDepartment of Orthopaedic and Spine Surgery, Fukuoka Children's Hospital, Fukuoka, Japan.
Toru YamaguchiDepartment of Orthopaedic and Spine Surgery, Fukuoka Children's Hospital, Fukuoka, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo develop regression models for predicting adolescent idiopathic scoliosis (AIS) progression using machine learning with clinical and radiographic data.

methodsWe included female patients aged 10 to 13 years diagnosed with AIS at our institution between 2007 and 2020. Only those with mild-to-moderate AIS (initial Cobb angle of 10°–40°) were analyzed. The objective variables were the Cobb angle at 1and 2 years later and at the last visit. A stacking regression metamodel with 5-fold cross-validation was developed using age, bracing, first menstruation, height, weight, Cobb angle at first visit, curve pattern, Risser sign, and kyphosis. Model performance was evaluated by mean absolute error (MAE) and R2 with internal validation by 1000 bootstrap samples.

resultsWe analyzed 201 patients. The median Cobb angle at the first visit was 27.0° (interquartile range, 21–32), and the median observation period was 44.0 months. The developed models achieved: MAE, 4.22 (standard deviation [SD]: 0.25), R2, 0.73 (SD: 0.03) for the 1-year Cobb angle prediction; MAE, 5.24 (SD: 0.34), R2, 0.67 (SD: 0.04) for the 2-year Cobb angle prediction; and MAE, 5.99 (SD: 0.35), R2, 0.73 (SD: 0.03) for the prediction at the last visit.

conclusionThe 1-year prediction outperformed previously reported models with shorter follow-up. Despite an extended observation period of nearly 4 years, prediction accuracy at the last follow-up remained clinically valuable, supporting the long-term reliability of the model.

Indexed as

Machine LearningScoliosisAdolescentChildDisease ProgressionFemaleHumansPrediction AlgorithmsPredictive Learning ModelsAdolescent idiopathic scoliosisMachine learningPrediction modelRegression model

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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.