External validation of a machine learning-based web application for personalized testing of objective functioning using the five-repetition sit-to-stand test
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Le résumé fourni par la source
PURPOSE: Simple generalized thresholds for assessing functional impairment in clinical testing are limited, as they fail to consider patient-specific properties such as age, body height, and body mass index. A previously developed machine learning-based model for personalized testing using the five-repetition sit-to-stand (5R-STS) test, that estimates personalized upper limits of normal (ULN) to identify objective functional impairment (OFI) was externally validated to evaluate its performance and generalizability across cohorts. METHODS: values. Additionally, a Bland-Altman analysis was performed to assess agreement between observed and expected values. Validation of the expected ULN involved comparing the proportion of individuals exceeding their personalized thresholds with the corresponding proportion based on the generalized threshold. Subgroup analyses by test setting and country of residence were additionally performed, along with a graphical assessment of model performance. RESULTS: of 0.064 (95% CI: -0.25 to 0.15). The Bland-Altman analysis demonstrated a mean bias of -1.1 s. Based on the personalized ULNs, OFI was classified in 17.5% of individuals, compared to 6.4% when using the generalized threshold of 10.4 s. These analyses indicated limited external generalization, with acceptable approximation for some faster and mid-range test times but systematic underestimation of slower test times. Multivariable regression analyses showed that remote testing was independently associated with greater prediction bias and higher odds of personalized ULN exceedance compared with supervised testing (adjusted mean difference: - 0.90 s; adjusted OR: 10.7). Exploratory country-of-residence analyses showed differences across the three largest national subgroups. 130 (76.1%) rated ease of use as excellent, and 145 (84.9%) rated clarity of instructions as excellent. 143 participants (83.6%) indicated that they prefer the 5R-STS over a battery of questionnaires. CONCLUSIONS: In the context of personalized testing, moving toward individually focused precision assessment of patients requires rigorous external validation to ensure the robustness of such applied computational methods. In this external validation, the model demonstrated limited generalization including a systematic underestimation of slower test times and insufficient personalized ULN calibration. These findings indicate that external validity of models derived from single-center data can be limited, underscoring the importance of comprehensive external validation and, potentially, multicenter retraining before clinical implementation.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- External validation of a machine learning-based web application for personalized testing of objective functioning using the five-repetition sit-to-stand test
- Date Crossref
- 18/08/2026
- Éditeur
- Springer Science and Business Media LLC
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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University Hospital Zurich Machine Intelligence in Clinical Neuroscience & Microsurgical Neuroanatomy (MICN) Laboratory pays non établi dans la noticeÉtablissement de santé
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Medical University of Białystok pays non établi dans la noticeUniversité ou école supérieure
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Karolinska Institutet pays non établi dans la noticeUniversité ou école supérieure
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Innsbruck Medical University pays non établi dans la noticeUniversité ou école supérieure
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Universität Innsbruck pays non établi dans la noticeUniversité ou école supérieure
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Medical University of Bialystok Department of Neurosurgery pays non établi dans la noticeUniversité ou école supérieure
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Department of Clinical Neuroscience pays non établi dans la noticeÉtablissement de santé
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Medical University of Innsbruck Department of Neurosurgery pays non établi dans la noticeUniversité ou école supérieure
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Löwenströmska Hospital Capio Spine Center Stockholm pays non établi dans la noticeÉtablissement de santé
Machine Intelligence in Clinical Neuroscience & Microsurgical Neuroanatomy (MICN) Laboratory — University Hospital Zurich, Medical University of Białystok et Karolinska Institutet, avec 6 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.