Evidence map›Paper›PMID 35356612›Full record

ArticleJournal of healthcare engineering2022

Observation on the Effect of Intelligent Machine-Assisted Surgery and Perioperative Nursing.

Liping Lei

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 It has been retracted, and should not be counted.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
0.8field-weighted citation impact, top 28% 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, 5 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

  • Retraction · 2023-08-16Concerns/Issues about Data · Concerns/Issues about Results and/or Conclusions · Concerns/Issues about Referencing/Attributions · Concerns/Issues about Peer Review · Informed/Patient Consent - None/Withdrawn · Investigation by Journal/Publisher · Investigation by Third Party · Lack of IRB/IACUC Approval and/or Compliance · Paper Mill · Computer-Aided Content or Computer-Generated Content · Unreliable Results and/or Conclusions · · See also: https://pubpeer.com/publications/CA6C9777527B3BDFD932630F19721C
  • Retracted
5 · Who and what money

Authors and funding

1 author at 1 institution in 1 country.

Liping LeiOperating Room, The Second Affiliated Hospital of University of South China, Hengyang 421001, Hunan, China.ORCID 0000-0002-0848-9408
University of South China · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Orthopedic surgery and care during the perioperative period are the key to the treatment of orthopedic diseases, which can quickly and effectively treat orthopedic diseases and can quickly recover during the perioperative period. Therefore, this paper focuses on the observation of the effect of intelligent machine-assisted surgery and perioperative care, combined with smart wearable devices and C-arm camera calibration; the details of the bone surgery are assisted by the machine, and then the recognition ability is accelerated by writing into the digital bone bank. Based on machine vision, CNN training and learning are designed to design a machine-assisted perioperative nursing method. This paper also designed a bone surgery test experiment and perioperative adverse event data analysis, combined with the data obtained from the experiment, designed a comparison experiment with traditional surgery and perioperative nursing. The experimental results show that the success rate of machine-assisted surgery is increased by nearly 2%-15% compared with traditional surgery; and the rehabilitation degree of machine-assisted perioperative nursing is 15.83% higher than that of traditional perioperative nursing.

Indexed as

Orthopedic ProceduresPerioperative NursingHumansLearning

Identifiers

PMID35356612
PMCPMC8959971
OpenAlexW4220676086

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.