Efficacy of Simulator-Based Slit Lamp Training for Medical Students: A Prospective, Randomized Trial.

Education Ophthalmology Simulation Slit lamp Virtual reality

Journal

Ophthalmology and therapy
ISSN: 2193-8245
Titre abrégé: Ophthalmol Ther
Pays: England
ID NLM: 101634502

Informations de publication

Date de publication:
Aug 2023
Historique:
received: 26 02 2023
accepted: 11 05 2023
medline: 9 6 2023
pubmed: 9 6 2023
entrez: 9 6 2023
Statut: ppublish

Résumé

Simulation training has an important role in medical education. In ophthalmology, simulation-based training has been shown to be significantly effective for surgical and diagnostic training in direct and indirect ophthalmoscopy. In this study, we analysed the effects of simulator-based slit lamp training. In this prospective controlled trial, medical students in their eighth semester at Saarland University Medical Center (n = 24) who had attended a 1-week ophthalmological internship were randomized into two groups: The traditional group (n = 12) was examined directly after the 1-week internship; the simulator group (n = 12) was trained with the slit lamp simulator before passing an objective structured clinical examination (OSCE). A masked ophthalmological faculty trainer assessed the students' slit lamp skills (maximum total score 42 points [pts]): preparation (5 pts), clinical examination (9.5 pts), assessment of findings (9.5 pts), diagnosis (3 pts), commentary on the examination approach (8 pts), measurement of structures (2 pts) and recognition of five diagnoses (5 pts). All students completed post-assessment surveys. Examination grades and survey responses were compared between the groups. The overall performance of the slit lamp OSCE was significantly better (p < 0.001) in the simulator group than in the traditional group (29.75 [7.88] vs. 17.00 [4.75]) with significantly higher scores for the preparation and assessment of slit lamp controls (5.0 [0.0] vs. 3.0 [3.5]; p = 0.008) and localization of relevant structures (6.75 [3.13] vs. 4.0 [1.5]; p = 0.008). Consistently higher scores, but not significant, were assigned for the description of structures found (4.5 [3.38] vs. 3.25 [2.13]; p = 0.09) and the correct diagnosis (3.0 [0.0] vs. 3.0 [0.0]; p = 0.48). Surveys reflected the students' subjectively perceived knowledge gain during the simulator training for slit lamp illumination techniques (p = 0.002), recognition (p < 0.001), and assessment of the correct localization of pathologies (p < 0.001). Slit lamp examination is an important diagnostic method in ophthalmology. Simulator-based training improved students' examination techniques for localizing anatomical structures and pathological lesions. The transfer of theoretical knowledge into practice can be achieved in a stress-free atmosphere.

Identifiants

pubmed: 37294522
doi: 10.1007/s40123-023-00733-w
pii: 10.1007/s40123-023-00733-w
pmc: PMC10287863
doi:

Types de publication

Journal Article

Langues

eng

Pagination

2171-2186

Informations de copyright

© 2023. The Author(s).

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Auteurs

Svenja Deuchler (S)

Augenzentrum Frankfurt, Georg-Baumgarten-Straße 3, 60549, Frankfurt am Main, Germany. s.deuchler@icloud.com.
Department of Ophthalmology, Saarland University Medical Center, 66424, Homburg/Saar, Germany. s.deuchler@icloud.com.

Yaser Abu Dail (YA)

Department of Ophthalmology, Saarland University Medical Center, 66424, Homburg/Saar, Germany.

Frank Koch (F)

Augenzentrum Frankfurt, Georg-Baumgarten-Straße 3, 60549, Frankfurt am Main, Germany.
Department of Ophthalmology, University Hospital, Goethe University Frankfurt, 60590, Frankfurt am Main, Germany.

Claudia Buedel (C)

Augenzentrum Frankfurt, Georg-Baumgarten-Straße 3, 60549, Frankfurt am Main, Germany.

Hanns Ackermann (H)

Institute of Biostatistics, Goethe University Frankfurt, Frankfurt am Main, Germany.

Elias Flockerzi (E)

Department of Ophthalmology, Saarland University Medical Center, 66424, Homburg/Saar, Germany.

Berthold Seitz (B)

Department of Ophthalmology, Saarland University Medical Center, 66424, Homburg/Saar, Germany.

Classifications MeSH