Evidence map›Paper›PMID 36246357›Full record

ReviewFrontiers in bioengineering and biotechnology2022

Discussion on the possibility of multi-layer intelligent technologies to achieve the best recover of musculoskeletal injuries: Smart materials, variable structures, and intelligent therapeutic planning.

Na Guo, Jiawen Tian, Litao Wang, Kai Sun, Lixin Mi, Hao Ming, Zhao Zhe, Fuchun Sun

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in bioengineering and biotechnology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.4field-weighted citation impact, top 45% 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

2 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 4 institutions in 1 country.

Na GuoDepartment of Computer Science and Technology, Tsinghua University, Beijing, China.
Jiawen TianDepartment of Computer Science and Technology, Tsinghua University, Beijing, China.
Litao WangCollege of Engineering, China Agricultural University, Beijing, China.
Kai SunDepartment of Biomedical Engineering, Tsinghua University, Beijing, China.
Lixin MiMusculoskeletal Department, Beijing Rehabilitation Hospital, Beijing, China.
Hao MingOrthopaedics, Chinese PLA General Hospital, Beijing, China.
Zhao ZheDepartment of Biomedical Engineering, Tsinghua University, Beijing, China.
Fuchun SunDepartment of Computer Science and Technology, Tsinghua University, Beijing, China.
Tsinghua University · CNChina Agricultural University · CNChina Rehabilitation Research Center · CNChinese PLA General Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Although intelligent technologies has facilitated the development of precise orthopaedic, simple internal fixation, ligament reconstruction or arthroplasty can only relieve pain of patients in short-term. To achieve the best recover of musculoskeletal injuries, three bottlenecks must be broken through, which includes scientific path planning, bioactive implants and personalized surgical channels building. As scientific surgical path can be planned and built by through AI technology, 4D printing technology can make more bioactive implants be manufactured, and variable structures can establish personalized channels precisely, it is possible to achieve satisfied and effective musculoskeletal injury recovery with the progress of multi-layer intelligent technologies (MLIT).

Indexed as

best recover of musculoskeletalintelligent therapeutic planningmulti-layer intelligent technologiessmart materialsvariable structures

Identifiers

PMID36246357
PMCPMC9561816
OpenAlexW4298144214

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.