Evidence map›Paper›PMID 35193536›Full record

ReviewBMC musculoskeletal disorders2022

Musculoskeletal care - at the confluence of data science, sensors, engineering, and computation.

Suchitra Kataria, Vinod Ravindran

Abstract readReview
In one paragraph

Review in BMC musculoskeletal disorders, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

2 authors.

Suchitra KatariaMelange Communications Pte Ltd, Singapore, Singapore.ORCID https://orcid.org/0000-0003-0890-0700
Vinod RavindranCentre for Rheumatology, Calicut, Kerala, 673009, India. drvinod12@gmail.com.ORCID http://orcid.org/0000-0001-5842-1877

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Data has always been integral to modern medicine in almost all aspects of patient care and the recent proliferation of data has opened up innumerable opportunities for all the stakeholders in trying to improve the quality of care and health outcomes including quality of life and rehabilitation. Greater usage and adoption of digital technologies have led to the convergence of health data in different forms - clinical, self-reported, electronic health records social media, etc. The application and utilization of patient data set continue to get broadened each day with greater availability and access. These are empowering newer cutting-edge solutions such as connected care and artificial intelligence, 3D printing and real-life mimicking prosthetics. The availability of data at micro and macro levels has the potential to act as a catalyst for personalized care based on behavioral, cultural, genetic, and psychological needs for patients with musculoskeletal disorders. Realistic algorithms coupled with biomarkers which can identify relevant interventions and alert the care providers regarding any deterioration. Although in the nascent stage currently, 3D printing, exoskeletons, and virtual rehabilitation hold tremendous potential of cost-effective, precise interventions for the patients.

Indexed as

Data ScienceMusculoskeletal DiseasesArtificial IntelligenceHumansPatient CareQuality of Life3D printingArtificial intelligenceClinical trialsConnected careData analyticsExoskeletonHuman dataInternet of medical thingsPatient carePatient-reported outcomesPersonalised carePopulation healthProstheticsResearch

Identifiers

PMID35193536
PMCPMC8863295

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.