Evidence map›Paper›PMID 35676952›Full record

ArticleComputational intelligence and neuroscience2022

Effect of Low-Dose Dexmedetomidine Combined with Lumbosacral Plexus Block Guided by Ultrasound Imaging Based on Image Segmentation Algorithm in Fracture Surgery.

Qiang Ji, Feng Wang, Qiang He, Yanhui Li, Yan Ma

RetractedOpen access · hybridAbstract readRetracted Publication
In one paragraph

Article in Computational intelligence and neuroscience, 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 It has been retracted, and should not be counted.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
0.3field-weighted citation impact, top 41% 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, 2 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

Qiang JiDepartment of Anesthesiology, Hebei Gucheng Hospital, Hengshui 253800, Hebei, China.ORCID https://orcid.org/0000-0001-6283-4770
Feng WangDepartment of Anesthesiology, Hebei Gucheng Hospital, Hengshui 253800, Hebei, China.ORCID https://orcid.org/0000-0003-4109-0068
Qiang HeOperation Room, Hebei Gucheng Hospital, Hengshui 253800, Hebei, China.ORCID https://orcid.org/0000-0002-5694-7274
Yanhui LiDepartment of Pain, Hebei Gucheng Hospital, Hengshui 253800, Hebei, China.ORCID https://orcid.org/0000-0001-6796-4646
Yan MaDepartment of Anesthesiology, Hebei Gucheng Hospital, Hengshui 253800, Hebei, China.ORCID https://orcid.org/0000-0002-6290-2240
Zoucheng People's Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The aim of this study was to analyze the application of ultrasound-guided low-dose dexmedetomidine combined with lumbosacral plexus block based on artificial intelligence algorithm in the surgical treatment of proximal femoral fractures. 104 patients with proximal femoral fractures were divided into 52 cases in the experimental group (ultrasound-guided lumbosacral plexus block combined with dexmedetomidine based on local fitting image segmentation algorithm) and 52 cases in the routine group (endotracheal intubation and inhalation combined with general anesthesia). An image segmentation algorithm based on local fitting was constructed to enhance the ultrasound image. It was found that in the routine group, the heart rate (HR), systolic blood pressure (SBP), and diastolic blood pressure (DBP) at the beginning of intravenous injection of dexmedetomidine, during skin incision, and half an hour after skin incision were significantly lower than those at admission (

Indexed as

DexmedetomidineFemoral FracturesArtificial IntelligenceHumansLumbosacral PlexusUltrasonographyDexmedetomidine

Identifiers

PMID35676952
PMCPMC9170447
OpenAlexW4281650196

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.