Evidence map›Paper›PMID 38525781›Full record

ArticleMethods and protocols2024

A Virtual Case Presentation Platform: Protocol Study.

Imad Alex Awada, Adina Magda Florea, Alexandru Scafa-Udriște

Abstract read
In one paragraph

Article in Methods and protocols, 2024. 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
–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

0 citing papers in PubMed.

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

3 authors.

Imad Alex AwadaFaculty of Automatic Control and Computers, National University of Science and Technology POLITEHNICA Bucharest, 060042 Bucharest, Romania.ORCID 0000-0002-5783-2428
Adina Magda FloreaFaculty of Automatic Control and Computers, National University of Science and Technology POLITEHNICA Bucharest, 060042 Bucharest, Romania.ORCID 0000-0001-7249-1871
Alexandru Scafa-UdrișteFaculty of Medicine, University of Medicine and Pharmacy "Carol Davila" Bucharest, 050474 Bucharest, Romania.

Funding

Ministry of Research, Innovation and Digitalization, CCDI-UEFISCDI project number PN-III-P2-2.1-PED-2021-2511, within PNCDI III
6 · The paper itself

Abstract

Gaining practical experience is indispensable for medical students. Therefore, when medical students were prevented access to hospitals during the COVID-19 pandemic in Romania, there was an urgent need to find a solution that would allow medical students to develop the skills they would usually develop in hospitals but without the need to be physically present in a hospital. This was the reason behind the idea of developing a Virtual Case Presentation Platform. The platform offers the possibility for medical students to reproduce virtually, in clinically valid scenarios, the diagnostic process and treatment recommendation, as well as the interactions with patients that usually take place in hospitals using natural language through speech and text. On the platform, the students receive valuable feedback from the professors about their performance. In order to reproduce the whole targeted experience for students, without missing anything, before starting the development of the platform, it was mandatory to identify and understand all the aspects that should be covered by the platform. The proposed platform covers the different aspects that have been identified for the diagnostic process and treatment recommendation. It enables medical students to develop essential skills for their future careers as doctors.

Indexed as

education in medical sciencee-learninghuman-computer interactionsimulation engineweb interfaces

Identifiers

PMID38525781
PMCPMC10961792

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.