Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

Keynote webinar | Spotlight on medication adherence

LIVE: Thursday 27th June 2024, 18:00-19:30 (CEST)
Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.
ADVANCED SEARCH
Log in
Top
Insights into Imaging
Published in: Insights into Imaging 1/2021

Open Access 01-12-2021 | Guideline

How to perform an excellent radiology board examination: a web-based checklist

Authors: Oğuz Dicle, Sema Özan, Hatice Şahin, Mustafa Seçil

Published in: Insights into Imaging | Issue 1/2021

Login to get access

Abstract

Background

Board exams are now considered as means of quality procedures that aim to keep the professional knowledge and skills of the physicians at the highest level. In addition, for an assessment to be scientifically valid, it has to be done within defined standards. Although there are different sources in this field, there is a need for a resource that details the steps required for the examinations to be performed perfectly, brings descriptions of the reasons for the procedure and associates the steps with assessment standards. Experts with national and international experience both in radiology and medical education contributed to the preparation of this checklist.

Results

The guide includes 174 elements to consider before, after the exam order and examination. From the perspective of assessment standards, it has been observed that the steps to be considered before the exam have a greater impact on the validity and reliability of the exam. The standard in which the questions are most associated was validity with 117 (67.24%) questions.

Conclusions

We think that our guide, which will be accessible in the web environment, will be useful to the teams with a development goal or just start the exam, the candidates who will take the exam and the examiners.
Literature
1.
Choudhry NK, Fletcher RH, Soumerai SB (2005) Systematic review: the relationship between clinical experience and quality of health care. Ann Intern Med 142:260–273CrossRef
2.
Davis DA, Mazmanian PE, Fordis M, Van Harrison R, Thorpe KE, Perrier L (2006) Accuracy of physician self-assessment compared with observed measures of competence: a systematic review. JAMA 296(9):1094–1102CrossRef
3.
Brennan TA, Horwitz RI, Duffy FD, Cassel CK, Goode LD, Lipner RS (2004) The role of physician specialty board certification status in the quality movement. JAMA 292(9):1038–1043CrossRef
4.
5.
6.
Boorman J, Mathysen D, Noël J et al (2014) Survey on European postgraduate medical assessments by the Council for European Medical Specialty Assessments (UEMS-CESMA). MedEdPublish 3:42
7.
8.
European Board of Radiology (EBR) (2018) The European Diploma in Radiology (EDiR): investing in the future of the new generations of radiologists. Insights Imaging 9:905–909CrossRef
9.
10.
11.
Dennick R, Wilkinson S, Purcell N (2009) Online eAssessment: AMEE Guide No. 39. Med Teach 31:192–206CrossRef
12.
Norcini JJ, McKinley DW (2007) Assessment methods in medical education. Teach Teach Educ 23:239–250CrossRef
13.
Amin Z, Seng CY, Eng KH (eds) (2006) Practical guide to medical student assessment. World Scientific Publishing, Singapore
14.
McAleer S (2001) Choosing assessment instruments. In: Dent JA (ed) A practical guide for medical teachers. Elsevier Health Sciences, Edinburgh, pp 303–313
15.
16.
17.
18.
Downing SM (2003) Validity: on the meaningful interpretation of assessment data. Med Educ 37:830–837CrossRef
19.
Wass V, Archer J (2011) Assessing learners. In: Dornan T, Mann K, Scherpbier J, Spencer J (eds) Medical education theory and practice. Elsevier Health Sciences, Edinburgh, pp 229–255
20.
21.
Sireci S, Padilla J-L (2014) Validating assessments: introduction to the special section. Psicothema 26(1):97–99PubMed
22.
23.
24.
Shumway JM, Harden RM (2003) AMEE Guide No. 25: the assessment of learning outcomes for the competent and reflective physician. Med Teach 25(6):569–584CrossRef
25.
26.
Schuwirth LWT, Van Der Vleuten CPM (2011) General overview of the theories used in assessment: AMEE Guide No. 57. Med teach 33:783–797CrossRef
27.
28.
29.
30.
31.
32.
33.
34.
Raymond MR, Grande JP (2019) A practical guide to test blueprinting. Med Teach 41:854–861CrossRef
35.
36.
37.
Swing SR (2007) The ACGME outcome project: retrospective and prospective. Med Teach 29:648–654CrossRef
38.
39.
Bandiera G, Sherbino J, Frank JR (2006) The CanMEDS assessment tools handbook. An introductory guide to assessment methods for the CanMEDS competencies. The Royal College of Physicians and Surgeons of Canada, Ottawa
40.
41.
42.
Hodges B, Hanson M, McNaughton N, Regehr G (2002) Creating, monitoring, and improving a psychiatry OSCE A guide for faculty. Acad Psychiatry 26:133–161CrossRef
43.
Khan KZ, Gaunt K, Ramachandran S, Pushkar P (2013) The Objective Structured Clinical Examination (OSCE): AMEE Guide No. 81. Part II: organisation & administration. Med Teach 35:e1447–e1463CrossRef
44.
45.
Wallace P (2007) Coaching standardized patients: for use in the assessment of clinical competence. Springer, New York
46.
Kachur EK, Kalet A, Hanley K (2013) Organizing OSCEs (and other SP exercises) in ten steps. In: Zabar S, Kachur E, Kalet A, Hanley K (eds) Objective structured clinical examinations. Springer, New York, pp 7–13
47.
Adamo G (2003) Simulated and standardized patients in OSCEs: achievements and challenges 1992–2003. Med Teach 25:262–270CrossRef
48.
49.
50.
Cleland JA, Abe K, Rethans JJ (2009) The use of simulated patients in medical education: AMEE Guide No 42. Med Teach 31:477–486CrossRef
51.
Wood TJ (2009) The effect of reused questions on repeat examinees. Adv Health Sci Educ 14:465–473CrossRef
52.
Callear D, King T (1997) Using computer-based tests for information science. ALT-J 5:27–32CrossRef
53.
Wrigley W, Van Der Vleuten CPM, Freeman A, Muijtjens A (2012) A systemic framework for the progress test: Strengths, constraints and issues: AMEE Guide No 71. Med Teach 34:683–697CrossRef
54.
McCoubrie P (2004) Improving the fairness of multiple-choice questions: a literature review. Med Teach 26:709–712CrossRef
55.
56.
57.
Downing SM, Tekian A, Yudkowsky R (2006) Procedures for establishing defensible absolute passing scores on performance examinations in health professions education. Teach Learn Med 18:50–57CrossRef
58.
McKinley DW, Norcini JJ (2014) How to set standards on performance-based examinations: AMEE Guide No. 85. Med Teach 36:97–110CrossRef
59.
Bandaranayake RC (2008) Setting and maintaining standards in multiple choice examinations: AMEE Guide No. 37. Med Teach 30:836–845CrossRef
60.
Cascarini L, Irani M (2005) Surviving a clinical exam: a guide for candidates. J R Soc Med 98:174–177CrossRef
61.
62.
63.
64.
Yeung E, Kulasagarem K, Woods N, Dubrowski A, Hodges B, Carnahan H (2016) Validity of a new assessment rubric for a short-answer test of clinical reasoning. BMC Med Educ 16:192CrossRef
Metadata
Title
How to perform an excellent radiology board examination: a web-based checklist
Authors
Oğuz Dicle
Sema Özan
Hatice Şahin
Mustafa Seçil
Publication date
01-12-2021
Publisher
Springer Berlin Heidelberg
Published in
Insights into Imaging / Issue 1/2021
Electronic ISSN: 1869-4101
DOI
https://doi.org/10.1186/s13244-020-00924-0

Other articles of this Issue 1/2021

Insights into Imaging 1/2021 Go to the issue

Educational Review

Osteoid osteoma: the great mimicker

Statement

European consensus on patient contact shielding

Original Article

Machine learning-based CT radiomics approach for predicting WHO/ISUP nuclear grade of clear cell renal cell carcinoma: an exploratory and comparative study

Educational Review

Primary breast tuberculosis: imaging findings of a rare disease

Original Article

Deep learning approach for automatic segmentation of ulna and radius in dual-energy X-ray imaging

Educational Review

A meta-analysis of the diagnostic performance of machine learning-based MRI in the prediction of axillary lymph node metastasis in breast cancer patients