Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 209

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Is robot-aided sensorimotor training in stroke rehabilitation a realistic option?

Volpe, Bruce T., Krebs, Hermano I., Hogan, Neville·2001·Current Opinion in Neurology

Abstract

Stroke is the leading cause of disability, despite continued advances in prevention and treatment techniques based on novel delivery of new fibrinolytic drugs. Improved medical treatment of the complications caused by acute stroke has contributed to decreased mortality, but 90% of the survivors have significant neurological deficits. Reducing the degree of permanent disability remains the goal of poststroke neuro-rehabilitation programs, and new approaches to impairment reduction through managing sensorimotor experience may contribute further to altering disability. Recent reports from a number of laboratories using enhanced sensorimotor training protocols, particularly those with robotic devices, have indicated modest success in reducing impairment and increasing motor power in the exercised limb of patients with stroke when compared with control individuals. Whether arming the therapist with new tools, especially robotic devices, to treat impairment is a realistic approach to modern interdisciplinary rehabilitation raises questions regarding the added value of impairment reduction, and under what conditions should scientific and clinical development of robotic studies continue.

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1097/00019052-200112000-00011

Cites

3 of 3 references verified0 citation errors · 0 not checkable

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.

A few free checks a day · papers already verified open instantly · results are public

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

Show all 3 references

Cited by

See all 209

Metadata

Authors
Volpe, B. T., Krebs, H. I., & Hogan, N.
Venue
Current Opinion in Neurology
Published
2001-12
Volume
14
Issue
6
Pages
745-752
ISSN
1350-7540 (ISSN-L)
OpenAlex
W1977373229
Cited by
209
References
3
FWCI
8.21
Type
journal article
Language
en
Open access
closed

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

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Is robot-aided sensorimotor training in stroke rehabilitation a realistic option? https://app.cochat.ai/papers/10.1097/00019052-200112000-00011
The paper
Volpe, B. T., Krebs, H. I., & Hogan, N. (2001). Is robot-aided sensorimotor training in stroke rehabilitation a realistic option? Current Opinion in Neurology. https://doi.org/10.1097/00019052-200112000-00011

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.