Evidence map›Paper›PMID 35463666›Full record

ArticleJournal of healthcare engineering2022

Application of Wearable Sensors in the Treatment of Cervical Spondylosis Radiculopathy with Acupuncture.

Lei Chi, Qian Zhang

RetractedOpen access · hybridAbstract readRetracted Publication
In one paragraph

Article in Journal of healthcare engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
2.7field-weighted citation impact, top 9% of its field
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

1 citing paper in PubMed, 3 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors at 1 institution in 1 country.

Lei ChiDepartment of Acupuncture and Moxibustion, Heilongjiang University of Chinese Medicine Second Affiliated Hospital, Harbin 150000, Heilongjiang, China.ORCID 0000-0003-2782-3990
Qian ZhangDepartment of Acupuncture and Moxibustion, Heilongjiang University of Chinese Medicine Second Affiliated Hospital, Harbin 150000, Heilongjiang, China.
Heilongjiang University of Chinese Medicine · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Research shows that cervical spondylosis radiculopathy (CSR) is the most common type of cervical spondylosis in clinic, and Chinese medicine treatment has obvious advantages, among which acupuncture therapy has received increasing attention. CSR has the characteristics of high incidence, long treatment time, and easy recurrence after treatment. In order to meet the different needs of different patients, this paper uses wearable sensors to collect patient dynamic data, extracts the action features of cervical spondylosis to design a scoring system, analyzes the input feature scores through a convolutional neural network (CNN) model, and then outputs personalized acupuncture treatment plan. The development status of wearable sensors at home and abroad is introduced, and the modules and functions of the wearable sensors are designed. The CNN network is used as the network model for classification and recognition. The experimental results show that the CNN model used in this paper has a high classification accuracy, achieving an accuracy of up to 97%, and can help produce an effective treatment plan. In order to determine whether the treatment plan output by the model is effective, each group of data is handed over to two cervical spondylosis experts for scoring, and then the final treatment plan is determined from 10 acupuncture plans. In our experiments, 9 out of 10 plans generated by the CNN model were the same as generated by the experts, which shows the effectiveness of the model.

Indexed as

Acupuncture TherapyRadiculopathySpondylosisWearable Electronic DevicesHumansTreatment Outcome

Identifiers

PMID35463666
PMCPMC9020947
OpenAlexW4224018210

What Socratic 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 Socratic graph.