Evidence map›Paper›PMID 38450121›Full record

ArticleProceedings of the Human Factors and Ergonomics Society ... Annual Meeting. Human Factors and Ergonomics Society. Annual meeting2023

PROGRESSIVE MEDICAL SIMULATION: AN ANALYSIS OF THE INTEGRATION OF PROGRESSIVE AND PERSONALIZED LEARNING IN CENTRAL LINE SIMULATORS.

Isra K Elsaadany, Jessica M Gonzalez-Vargas, Jason Z Moore, Scarlett R Miller

Open access · hybridAbstract read
In one paragraph

Article in Proceedings of the Human Factors and Ergonomics Society ... Annual Meeting. Human Factors and Ergonomics Society. Annual meeting, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.2field-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

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

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

4 authors at 1 institution in 1 country.

Isra K ElsaadanyPennsylvania State University Industrial Engineering.
Jessica M Gonzalez-VargasPennsylvania State University Industrial Engineering.
Jason Z MoorePennsylvania State University Mechanical Engineering.
Scarlett R MillerPennsylvania State University Industrial Engineering.
Pennsylvania State University · 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

Progressive learning gradually increases task difficulty as students advance in their education. One area that can benefit from it is medical education since it can optimize medical trainees' skill acquisition. While progressive learning can allow for skill transfer to patient encounters, personalized learning increases the efficiency and effectiveness of learning. However, it is not well understood the number of practice trials needed to reach proficiency. To evaluate whether progressive and personalized learning can enhance medical trainees' learning gains, the learning interface of the Dynamic Haptic Robotic Trainer (DHRT) for Central Venous Catheterization was assessed. Results showed that residents' performance on the DHRT did not differ based on task difficulty and residents' performance was as effective with less number of trials. The findings imply a need to integrate progressive and personalized learning on the DHRT simulator to ensure that residents are fully prepared for any patient scenario in a real-life encounter.

Identifiers

PMID38450121
PMCPMC10914344
OpenAlexW4387937596

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.