Evidence map›Paper›PMID 38982137›Full record

ArticleScientific reports2024

Prediction of isometric forces from combined epidural spinal cord and neuromuscular electrical stimulation in the rat lower limb.

Daniel Song, Matthew C Tresch

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors.

Daniel SongDepartment of Biomedical Engineering, Northwestern University, Chicago, IL, 60611, USA. dan.song.72@gmail.com.
Matthew C TreschDepartment of Biomedical Engineering, Northwestern University, Chicago, IL, 60611, USA.

Funding

Optimizing the restoration and rehabilitation of function using cortically-controlled FES following SCIR01NS112535 · NINDS · NORTHWESTERN UNIVERSITY AT CHICAGO · PI TRESCH, MATTHEW · 2019 to 2023
$2.6M
NIH HHS 5R01NS112535NINDS NIH HHS R01 NS112535The Graduate School, Northwestern University Christina Enroth-Cugell and David Cugell Fellowship Fund
6 · The paper itself

Abstract

Although epidural spinal cord and muscle stimulation have each been separately used for restoration of movement after spinal cord injury, their combined use has not been widely explored. Using both approaches in combination could provide more flexible control compared to using either approach alone, but whether responses evoked from such combined stimulation can be easily predicted is unknown. We evaluate whether responses evoked by combined spinal and muscle stimulation can be predicted simply, as the linear summation of responses produced by each type of stimulation individually. Should this be true, it would simplify the prediction of co-stimulation responses and the development of control schemes for spinal cord injury rehabilitation. In healthy anesthetized rats, we measured hindlimb isometric forces in response to spinal and muscle stimulation. Force prediction errors were calculated as the difference between predicted and observed co-stimulation forces. We found that spinal and muscle co-stimulation could be closely predicted as the linear summation of the individual spinal and muscle responses and that the errors were relatively low. We discuss the implications of these results to the use of combined muscle and spinal stimulation for the restoration of movement following spinal cord injury.

Indexed as

Isometric ContractionMuscle, SkeletalSpinal CordSpinal Cord InjuriesAnimalsElectric StimulationElectric Stimulation TherapyEpidural SpaceFemaleHindlimbLower ExtremityRatsRats, Sprague-DawleySpinal Cord Stimulation

Identifiers

PMID38982137
PMCPMC11233659

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.