Evidence map›Paper›PMID 33937348›Full record

ArticleFrontiers in robotics and AI2021

Motion Smoothness Metrics for Cannulation Skill Assessment: What Factors Matter?

Simar Singh, Joe Bible, Zhanhe Liu, Ziyang Zhang, Ravikiran Singapogu

Open access · goldAbstract read
In one paragraph

Article in Frontiers in robotics and AI, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
2.3field-weighted citation impact, top 12% 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

10 citing papers in PubMed, 14 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Computer Assisted Objective Assessment of Micro-Neurosurgical Skills From Intraoperative Videos.IEEE open journal of engineering in medicine and biology · 2023
    Article
  9. Article
  10. Simulator-Based Metrics for Quantifying Vascular Palpation Skill for Cannulation.IEEE access : practical innovations, open solutions · 2022
    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

5 authors at 1 institution in 1 country.

Simar SinghDepartment of Bioengineering, Clemson University, Clemson, SC, United States.
Joe BibleDepartment of Mathematical and Statistical Sciences, Clemson University, Clemson, SC, United States.
Zhanhe LiuDepartment of Bioengineering, Clemson University, Clemson, SC, United States.
Ziyang ZhangDepartment of Bioengineering, Clemson University, Clemson, SC, United States.
Ravikiran SingapoguDepartment of Bioengineering, Clemson University, Clemson, SC, United States.
Clemson University · US

Funding

CanSim: A novel simulator for training cannulation skills in dialysis careK01DK111767 · NIDDK · CLEMSON UNIVERSITY · PI SINGAPOGU, RAVIKIRAN · 2016 to 2020
$660k
NIDDK NIH HHS K01 DK111767
6 · The paper itself

Abstract

Medical training simulators have the potential to provide remote and automated assessment of skill vital for medical training. Consequently, there is a need to develop "smart" training devices with robust metrics that can quantify clinical skills for effective training and self-assessment. Recently, metrics that quantify motion smoothness such as log dimensionless jerk (

Indexed as

cannulationlog dimensionless jerkmedical training simulatormotion smoothnessskill metricsspectral arc length

Identifiers

PMID33937348
PMCPMC8085519
OpenAlexW3156108667

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.