Observational studyArchives of physical medicine and rehabilitation2022
Accuracy of an Algorithm in Predicting Upper Limb Functional Capacity in a United States Population.
Observational study in Archives of physical medicine and rehabilitation, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 8 papers.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 22 citations in OpenAlex.
- The Accuracy of the PREP2 Prediction Tool for Upper Limb Outcomes After Stroke as Part of Routine Clinical Care.Neurorehabilitation and neural repair · 2026Observational
- Upper Limb Function 3 Months Post-Stroke: How Accurate Are Physiotherapist Predictions?Physiotherapy research international : the journal for researchers and clinicians in physical therapy · 2025Article
- Accuracy and Reliability of Remote Categorization of Upper Limb Outcome After Stroke.Neurorehabilitation and neural repair · 2024Article
- Early Prediction of Poststroke Rehabilitation Outcomes Using Wearable Sensors.Physical therapy · 2024Article
- Predicting later categories of upper limb activity from earlier clinical assessments following stroke: an exploratory analysis.Journal of neuroengineering and rehabilitation · 2023Article
- Computer-assisted approaches for measuring, segmenting, and analyzing functional upper extremity movement: a narrative review of the current state, limitations, and future directions.Frontiers in rehabilitation sciences · 2023Review
- Early prediction of upper limb functioning after stroke using clinical bedside assessments: a prospective longitudinal study.Scientific reports · 2022Article
- Article
Corrections and comments
- Erratum issued
Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
objectiveTo determine the accuracy of an algorithm, using clinical measures only, on a sample of persons with first-ever stroke in the United States (US). It was hypothesized that algorithm accuracy would fall in a range of 70%-80%.
designSecondary analysis of prospective, observational, longitudinal cohort; 2 assessments were done: (1) within 48 hours to 1 week poststroke and (2) at 12 weeks poststroke.
settingRecruited from a large acute care hospital and followed over the first 6 months after stroke.
participantsAdults with first-ever stroke (N=49) with paresis of the upper limb (UL) at ≤48 hours who could follow 2-step commands and were expected to return to independent living at 6 months.
interventionNot applicable.
main outcome measuresThe overall accuracy of the algorithm with clinical measures was quantified by comparing predicted (expected) and actual (observed) categories using a correct classification rate.
resultsThe overall accuracy (61%) and weighted κ (62%) were significant. Sensitivity was high for the Excellent (95%) and Poor (81%) algorithm categories. Specificity was high for the Good (82%), Limited (98%), and Poor (95%) categories. Positive predictive value (PPV) was high for Poor (82%) and negative predictive value (NPV) was high for all categories. No differences in participant characteristics were found between those with accurate or inaccurate predictions.
conclusionsThe results of the present study found that use of an algorithm with clinical measures only is better than chance alone (chance=25% for each of the 4 categories) at predicting a category of UL capacity at 3 months post troke. The moderate to high values of sensitivity, specificity, PPV, and NPV demonstrates some clinical utility of the algorithm within health care settings in the US.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.