ArticleTopics in spinal cord injury rehabilitation2023
Developing and Evaluating Data Infrastructure and Implementation Tools to Support Cardiometabolic Disease Indicator Data Collection.
Article in Topics in spinal cord injury rehabilitation, 2023. 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, 1 citations in OpenAlex.
- Screening, Diagnosis, and Treatment of Cardiometabolic Risk in Persons With Spinal Cord Injury/Disease: A Scoping Review.Archives of rehabilitation research and clinical translation · 2025Review
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Assessment of aerobic exercise (AE) and lipid profiles among individuals with spinal cord injury or disease (SCI/D) is critical for cardiometabolic disease (CMD) risk estimation. Objectives: To utilize an artificial intelligence (AI) tool for extracting indicator data and education tools to enable routine CMD indicator data collection in inpatient/outpatient settings, and to describe and evaluate the recall of AE levels and lipid profile assessment completion rates across care settings among adults with subacute and chronic SCI/D. Methods: A cross-sectional convenience sample of patients affiliated with University Health Network's SCI/D rehabilitation program and outpatients affiliated with SCI Ontario participated. The SCI-HIGH CMD intermediary outcome (IO) and final outcome (FO) indicator surveys were administered, using an AI tool to extract responses. Practice gaps were prospectively identified, and implementation tools were created to address gaps. Univariate and bivariate descriptive analyses were used. Results: The AI tool had <2% error rate for data extraction. Adults with SCI/D ( Conclusion: Compelling CMD health service gaps warrant immediate attention to achieve AE and lipid assessment guideline adherence. AI indicator extraction paired with implementation tools may facilitate indicator deployment and modify CMD risk.
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