ArticleAnnals of medicine2026
Construction and verification of a nomogram model for predicting the risk of post-stroke spasticity: a retrospective study.
Article in Annals of medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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Who cites it
1 citing paper in PubMed.
- Machine learning-based prediction of meniscal tears in ACL reconstruction using BMI, time to surgery, injury mechanism, and Tegner activity score: A temporally validated decision tool.Journal of experimental orthopaedics · 2026Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
resultsLASSO-logistic regression analysis identified seven predictors associated with PSS: C-reactive protein, albumin, creatine kinase, fasting blood glucose, hyperlipidemia, sleep disorders, and manual muscle testing (MMT) score at admission. The model had an area under the curve (AUC) of 0.844 (95% CI: 0.793-0.896) in the training set and 0.842 (95% CI: 0.765-0.920) in the validation set, which means it was good at making predictions. The calibration curves showed excellent agreement between predicted and observed probabilities in the training set. Good calibration was maintained in the validation set, indicating only minimal overestimation of risk. DCA and CIC both agreed that the nomogram model could be used in a wide range of therapeutic situations.
conclusionThe nomogram based on routine clinical data in this study, after internal validation, can effectively predict the risk of PSS and provides a practical decision-making tool for clinicians. However, future multi-centre external validation is still required to confirm its broad applicability.
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Registered trials
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