Evidence map›Paper›PMID 30502270›Full record

ArticleThe Journal of surgical research2019

Evaluating Surgical Resident Needle Insertion Skill Gains in Central Venous Catheterization Training.

Hong-En Chen, Mary A Yovanoff, David F Pepley, Rohan S Prabhu, Cheyenne C Sonntag, David C Han, Jason Z Moore, Scarlett R Miller

Open access · greenAbstract readEvaluation Study
In one paragraph

Article in The Journal of surgical research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
2.1field-weighted citation impact, top 13% 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

7 citing papers in PubMed, 14 citations in OpenAlex.

  1. Cannulation Skill Assessment Using Functional Data Analysis.IEEE journal of biomedical and health informatics · 2023
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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 2 institutions in 1 country.

Hong-En ChenDepartment of Industrial and Manufacturing Engineering, Pennsylvania State University, University Park, Pennsylvania.
Mary A YovanoffDepartment of Industrial and Manufacturing Engineering, Pennsylvania State University, University Park, Pennsylvania.
David F PepleyDepartment of Mechanical and Nuclear Engineering, Pennsylvania State University, University Park, Pennsylvania.
Rohan S PrabhuDepartment of Mechanical and Nuclear Engineering, Pennsylvania State University, University Park, Pennsylvania.
Cheyenne C SonntagDepartment of Surgery, Penn State Health Milton S. Hershey Medical Center, Hershey, Pennsylvania.
David C HanDepartment of Surgery, Penn State Health Milton S. Hershey Medical Center, Hershey, Pennsylvania; Penn State Heart and Vascular Institute, Penn State Health Milton S. Hershey Medical Center, Hershey, Pennsylvania.
Jason Z MooreDepartment of Mechanical and Nuclear Engineering, Pennsylvania State University, University Park, Pennsylvania.
Scarlett R MillerDepartment of Industrial and Manufacturing Engineering, Pennsylvania State University, University Park, Pennsylvania; School of Engineering Design, Technology and Professional Programs, Pennsylvania State University, University Park, Pennsylvania. Electronic address: scarlettmiller@psu.edu.
Pennsylvania State University · USPenn State Milton S. Hershey Medical Center · US

Funding

Dynamic Haptic Robotic Training for Central Venous Catheter InsertionR01HL127316 · NHLBI · PENNSYLVANIA STATE UNIV HERSHEY MED CTR · PI Scarlett Miller, Jason Zachary Moore · 2015 to 2026
$3.7M
NHLBI NIH HHS R01 HL127316
6 · The paper itself

Abstract

backgroundTraining for ultrasound-guided central venous catheterization (CVC) is typically conducted on static manikin simulators with real-time feedback from a skilled observer. Dynamic haptic robotic trainers (DHRTs) are an alternative method that simulates various patient anatomies and provides consistent feedback for each insertion. This study evaluates CVC needle insertion efficiency and skill gains of both methods. MATERIALS AND

methodsFifty-two first-year surgical residents were trained by placing internal jugular (IJ) CVC needles in manikins (n = 26) or robots (n = 26). Manikin-trained participants received verbal feedback from an experienced observer, whereas robotically trained participants received quantitative feedback from the personalized DHRT learning interface. All participants were pretested on a Blue Phantom manikin; participants completed posttesting on a Blue Phantom manikin (n = 26) or a novel manikin (n = 26) with different vessel depth and position. During pretests and posttests residents were timed, motion-tracked, and scored on an IJ CVC checklist.

results(1) All skills on the IJ CVC checklist showed significant (P < 0.014) improvements from pretests to posttest; (2) Average angle of insertion, path length, and jerk improved significantly (P < 0.005); (3) Average procedural completion time, with standard error (SE) reported, decreased significantly from pretest (M = 3.516 min, SE = 0.277) to posttest (M = 1.997, SE = 0.409).

conclusionsNo significant group differences were observed in overall skill gains, but residents' average procedural completion time decreased significantly from pretests to posttest. Overall results support DHRT as an effective method for training IJ CVC skills.

Indexed as

Catheterization, Central VenousClinical CompetenceEducational MeasurementEducation, Medical, GraduateFemaleGeneral SurgeryHumansInternship and ResidencyMaleManikinsNeedlesRoboticsSimulation TrainingTime FactorsUltrasonography, InterventionalCentral venous catheterizationNeedle insertionRobotic simulationSurgical simulationTraining and assessmentUltrasound manikins

Identifiers

PMID30502270
PMCPMC6290357
OpenAlexW2892014946

What OpenQuestion holds

Textmetadata
LicenceTDM
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.