ArticleScientific reports2020
Transforming self-reported outcomes from a stroke register to the modified Rankin Scale: a cross-sectional, explorative study.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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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.
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Who cites it
6 citing papers in PubMed.
- Trial
- Beyond cardiac embolism and cryptogenic stroke: unveiling the mechanisms of cerebrovascular events in Chagas disease.Lancet regional health. Americas · 2025Review
- Professional reintegration of stroke survivors and their mental health, quality of life and community integration.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2024Article
- Feasibility, Fidelity and Acceptability of a Person-Centred Care Transition Support Intervention for Stroke Survivors: A Non-Randomised Controlled Study.Health expectations : an international journal of public participation in health care and health policy · 2024Article
- Prediction of long-term functional outcome following different rehabilitation pathways after stroke unit discharge.Journal of rehabilitation medicine · 2024Article
- The precision by the Face Arm Speech Time (FAST) algorithm in stroke capture, sex and age differences: a stroke registry study.BMJ neurology open · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim was to create an algorithm to transform self-reported outcomes from a stroke register to the modified Rankin Scale (mRS). Two stroke registers were used: the Väststroke, a local register in Gothenburg, Sweden, and the Riksstroke, a Swedish national register. The reference variable, mRS (from Väststroke), was mapped with seven self-reported questions from Riksstroke. The transformation algorithm was created as a result of manual mapping performed by healthcare professionals. A supervised machine learning method-decision tree-was used to further evaluate the transformation algorithm. Of 1145 patients, 54% were male, the mean age was 71 y. The mRS grades 0, 1 and 2 could not be distinguished as a result of manual mapping or by using the decision tree analysis. Thus, these grades were merged. With manual mapping, 78% of the patients were correctly classified, and the level of agreement was almost perfect, weighted Kappa (K
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Registered trials
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