The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Determining Levels of Upper Extremity Movement Impairment by Applying a Cluster Analysis to the Fugl-Meyer Assessment of the Upper Extremity in Chronic Stroke
Open accessAbstract
Objective To quantitatively define levels of upper extremity movement impairment using cluster analysis of Fugl-Meyer upper extremity (FM-UE) with and without reflex items. Design Secondary analysis of FM-UE individual item scores compiled from baseline testing of 5 studies with consistent testing procedures. Setting University and VA research centers. Participants: Individuals (N=−247) with chronic stroke (>6 months post-stroke). Interventions Not applicable. Main Outcome Measures Cut-off scores defined by total FM-UE scores of clusters identified by two hierarchical cluster analyses run on full sample of FM-UE individual item scores (with/without reflexes). Patterns of motor function defined by aggregate item scores of clusters. Results FM-UE scores ranged from 2–63 (mean=26.9±15.7) with reflex items and 0–57 (mean=22.1 ±15.3) without reflex items. Three clusters were identified. The distributions of the FM-UE scores revealed considerable overlap between the clusters, therefore four distinct stroke impairment levels were also derived. Conclusions For chronic stroke, the cluster analyses of the upper extremity FM support either a three or a four impairment level classification scheme.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
More Outcomes than Trials: A Call for Consistent Data Collection across Stroke Rehabilitation Trials
Ali, M., English, C., Bernhardt, J., Sunnerhagen, K. S., & Brady, M. (2013) · International Journal of Stroke · doi:10.1111/j.1747-4949.2012.00973.x · Cited by 47
Cited by
Oubre, B., Daneault, J.-F., Jung, H.-T., Whritenour, K., Miranda, J. G. V., Park, J., Ryu, T., Kim, Y., & Lee, S. I. (2020) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2020.2966950 · Cited by 78
Jovanovic, L. I., Kapadia, N., Lo, L., Zivanovic, V., Popovic, M. R., & Marquez-Chin, C. (2020) · American Journal of Physical Medicine & Rehabilitation · doi:10.1097/phm.0000000000001163 · Cited by 36
Metadata
- Authors
- Woytowicz, E. J., Rietschel, J. C., Goodman, R. N., Conroy, S. S., Sorkin, J. D., Whitall, J., & McCombe Waller, S.
- Venue
- Archives of Physical Medicine and Rehabilitation
- Publisher
- Elsevier BV
- Published
- 2017-03
- Volume
- 98
- Issue
- 3
- Pages
- 456-462
- ISSN
- 0003-9993 (ISSN-L)
- OpenAlex
- W2527738678
- Cited by
- 347
- References
- 1
- FWCI
- 9.90
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.