Evidence map›Paper›PMID 38229942›Full record

ReviewWorld journal of methodology2023

Machine learning and deep neural network-based learning in osteoarthritis knee.

Harish V K Ratna, Madhan Jeyaraman, Naveen Jeyaraman, Arulkumar Nallakumarasamy, Shilpa Sharma, Manish Khanna, Ashim Gupta

Open access · diamondAbstract readReview
In one paragraph

Review in World journal of methodology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
0.7field-weighted citation impact, top 25% 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

4 citing papers in PubMed, 1 synthesis or guideline pooled it, 3 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
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

7 authors at 2 institutions in 1 country.

Harish V K RatnaDepartment of Orthopaedics, Rathimed Speciality Hospital, Chennai 600040, Tamil Nadu, India.
Madhan JeyaramanDepartment of Orthopaedics, ACS Medical College and Hospital, Dr MGR Educational and Research Institute, Chennai 600077, Tamil Nadu, India.
Naveen JeyaramanDepartment of Orthopaedics, ACS Medical College and Hospital, Dr MGR Educational and Research Institute, Chennai 600077, Tamil Nadu, India.
Arulkumar NallakumarasamyDepartment of Orthopaedics, ACS Medical College and Hospital, Dr MGR Educational and Research Institute, Chennai 600077, Tamil Nadu, India.
Shilpa SharmaDepartment of Paediatric Surgery, All India Institute of Medical Sciences, New Delhi 110029, India.
Manish KhannaDepartment of Orthopaedics, Autonomous State Medical College, Ayodhya 224133, Uttar Pradesh, India.
Ashim GuptaDepartment of Orthopaedics, South Texas Orthopaedic Research Institute, Laredo, TX 78045, United States.
ACS Medical College and Hospital · INAll India Institute of Medical Sciences · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Osteoarthritis (OA) of the knee joint is considered the commonest musculoskeletal condition leading to marked disability for patients residing in various regions around the globe. Application of machine learning (ML) in doing research regarding OA has brought about various clinical advances viz, OA being diagnosed at preliminary stages, prediction of chances of development of OA among the population, discovering various phenotypes of OA, calculating the severity in OA structure and also discovering people with slow and fast progression of disease pathology,

Indexed as

Artificial intelligenceDeep neural networkKneeMachine learningOsteoarthritis

Identifiers

PMID38229942
PMCPMC10789099
OpenAlexW4389965911

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.