Evidence map›Paper›PMID 36711530›Full record

ArticlebioRxiv : the preprint server for biology2023

A machine learning approach to quantify individual gait responses to ankle exoskeletons.

Megan R Ebers, Michael C Rosenberg, J Nathan Kutz, Katherine M Steele

Open access · greenAbstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2023. 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, 2 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors at 3 institutions in 1 country.

Megan R EbersDepartment of Mechanical Engineering, University of Washington, Seattle, WA, 98195, USA.
Michael C RosenbergDepartment of Mechanical Engineering, University of Washington, Seattle, WA, 98195, USA.
J Nathan KutzDepartment of Applied Mathematics, University of Washington, Seattle, WA, 98195, USA.
Katherine M SteeleDepartment of Mechanical Engineering, University of Washington, Seattle, WA, 98195, USA.
University of Washington · USGeorgia Institute of Technology · USUniversity of Washington Applied Physics Laboratory · US

Funding

Modulation of gait dynamics post-strokeF32HD108927 · NICHD · EMORY UNIVERSITY · PI ROSENBERG, MICHAEL CHARLES · 2022 to 2024
$227k
NICHD NIH HHS F32 HD108927
6 · The paper itself

Abstract

We currently lack a theoretical framework capable of characterizing heterogeneous responses to exoskeleton interventions. Predicting an individual's response to an exoskeleton and understanding what data are needed to characterize responses has been a persistent challenge. In this study, we leverage a neural network-based discrepancy modeling framework to quantify complex changes in gait in response to passive ankle exoskeletons in nondisabled adults. Discrepancy modeling aims to resolve dynamical inconsistencies between model predictions and real-world measurements. Neural networks identified models of (i)

Identifiers

PMID36711530
PMCPMC9882260
OpenAlexW4317655876

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.