ArticleStroke research and treatment2017
Sequence Analysis of Long-Term Readmissions among High-Impact Users of Cerebrovascular Patients.
Article in Stroke research and treatment, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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.
The trial behind it
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
1 citing paper in PubMed.
- Analytical methods for identifying sequences of utilization in health data: a scoping review.BMC medical research methodology · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveUnderstanding the chronological order of the causes of readmissions may help us assess any repeated chain of events among high-impact users, those with high readmission rate. We aim to perform sequence analysis of administrative data to identify distinct sequences of emergency readmissions among the high-impact users.
methodsA retrospective cohort of all cerebrovascular patients identified through national administrative data and followed for 4 years.
resultsCommon discriminating subsequences in chronic high-impact users (
conclusionAlthough common causes of readmission are the same in different subgroups, the high-impact users had a higher proportion of patients with distinct common sequences of multiple readmissions as identified by the sequence analysis. Most of these causes are potentially preventable and can be avoided in the community.
Identifiers
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Registered trials
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