Structured Autopsy-based Case-based Learning in Competency-based Medical Education: A Prospective Randomized Controlled Trial
DOI:
https://doi.org/10.48165/jfmt.2026.43.03.14Keywords:
CBME, Case Based Learning, autopsyAbstract
Background: The introduction of Competency-Based Medical Education (CBME) led to a transition from traditional didactic lecturing to active, outcome-oriented learning model. The aim is to prioritize clinical reasoning and demonstrable skills. In forensic medicine, theoretical knowledge of mechanical injuries needs to be translated into diagnostic reasoning which requires clinical correlation.
Aims: To compare the effectiveness of structured autopsy-based case-based learning (Autopsy-CBL) versus traditional lecture-based learning (LBL) for teaching mechanical injury competencies to 180 second- and third-professional undergraduate medical students. This study evaluates psychometric validity, short-term knowledge acquisition, long-term knowledge retention, learner perceptions, and institutional feasibility . It also addresses common methodological limitations in medical education research.
Materials and Methods: A prospective, parallel-group, randomized comparative educational intervention was conducted over a six-month academic period. Students were randomly assigned to either LBL () or Autopsy-CBL (). Both cohorts received identical curricular content. Interventions were designed and reported strictly utilizing the Template for Intervention Description and Replication (TIDieR) framework. Knowledge acquisition and two-month retention were assessed using a 25-item multiple-choice questionnaire (MCQ). This assessment tool was subjected to psychometric validation, including blueprinting, expert review for the Content Validity Index (CVI), Item Difficulty (DIF), Discrimination Index (DIS), Distractor Efficiency (DE), and KR-20 reliability. Data were analyzed using a baseline-adjusted Analysis of Covariance (ANCOVA) after verifying all statistical assumptions. Institutional feasibility was evaluated using the Bowen framework.
Results: The psychometric validation yielded a highly reliable instrument (KR-20 = 0.82) with excellent discrimination parameters. Baseline knowledge was statistically comparable between groups (). One week post-intervention, the Autopsy-CBL group achieved significantly higher adjusted mean scores than the LBL group (Adjusted Mean Difference = 2.75, 95% CI 2.01 to 3.49; ), yielding a large standardized effect size (Hedges' ; 95% CI 0.69 to 1.30). Group-wise retention analysis at two months demonstrated significant mitigation of knowledge decay in the Autopsy-CBL cohort compared to the LBL cohort (). A validated learner perception survey revealed that students strongly favored Autopsy-CBL across engagement, visualization, and clinical correlation domains. The educational protocol fulfilled all Bowen framework indicators for feasibility, demand, and practical implementation. This was done within existing institutional constraints.
Conclusion: Structured Autopsy-CBL was feasible to implement within the existing mortuary setting. It resulted in significantly better short-term knowledge acquisition and two-month knowledge retention than traditional lecture-based learning. The structured approach also helped shift mortuary teaching from passive observation toward active, case-based learning. This was consistent with the principles of CBME.
Downloads
References
National Medical Commission. Guidelines for Competency Based Medical Education (CBME) Curriculum 2024. New Delhi: National Medical Commission; 2024. No. D-11011/500/2024-AcademicCell (e-8284443), dated 12 September 2024.
Frank JR, Snell LS, ten Cate O, Holmboe ES, Carraccio C, Swing SR, et al. Competency-based medical education: theory to practice. Med Teach. 2010;32(8):638-645. doi:10.3109/0142159X.2010.501190.
Harden RM. Outcome-Based Education: the future is today. Med Teach. 2007;29(7):625-629. doi:10.1080/01421590701729930.
Freeman S, Eddy SL, McDonough M, Smith MK, Okoroafor N, Jordt H, et al. Active learning increases student performance in science, engineering, and mathematics. Proc Natl Acad Sci U S A. 2014;111(23):8410-8415. doi:10.1073/pnas.1319030111.
Srinivasan M, Wilkes M, Stevenson F, Nguyen T, Slavin S. Comparing problem-based learning with case-based learning: effects of a major curricular shift at two institutions. Acad Med. 2007;82(1):74-82. doi:10.1097/01.ACM.0000249963.93776.aa.
Thistlethwaite JE, Davies D, Ekeocha S, Kidd JM, MacDougall C, Matthews P, et al. The effectiveness of case-based learning in health professional education: a BEME systematic review. Med Teach. 2012;34(6):e421-e444. doi:10.3109/0142159X.2012.680939.
Dochy F, Segers M, Van den Bossche P, Gijbels D. Effects of problem-based learning: a meta-analysis. Learn Instr. 2003;13(5):533-568.
Sharma P, Solanki A, Meena PC, Suryabhan, Vijeyta, Pathak D. Student’s perception on case based learning for competency based medical education in forensic medicine. Int J Curr Pharm Rev Res. 2025;17(3):1953-1955.
Burton JL. The autopsy in modern undergraduate medical education: a qualitative study of uses and curriculum considerations. Med Educ. 2003;37(12):1073-1081. doi:10.1046/j.1365-2923.2003.01710.x.
Pakanen L, Tikka J, Kuvaja P, Lunetta P. Autopsy-Based Learning is Essential But Underutilized in Medical Education: A Questionnaire Study. Anat Sci Educ. 2022;15(2):341-351. doi:10.1002/ase.2073.
Bamber AR, Quince TA. The value of postmortem experience in undergraduate medical education: current perspectives. Adv Med Educ Pract. 2015;6:159-170. doi:10.2147/AMEP.S46669.
Tandon A, Kanchan T, Atreya A, Tandon A. Perceptions of medical students towards autopsy teaching and its significance. Med Sci Law. 2019;59(3):143-148. doi:10.1177/0025802419855448.
Dirnhofer R, Jackowski C, Vock P, Potter K, Thali MJ. VIRTOPSY: minimally invasive, imaging-guided virtual autopsy. Radiographics. 2006;26(5):1305-1333. doi:10.1148/rg.265065001.
Joseph TI, Girish KL, Sathyan P, Kiran MS, Vidya S. Virtopsy: an integration of forensic science and imageology. J Forensic Dent Sci. 2017;9(3):111-114. doi:10.4103/jfo.jfds_52_16.
Young JQ, Van Merrienboer J, Durning S, Ten Cate O. Cognitive Load Theory: implications for medical education: AMEE Guide No. 86. Med Teach. 2014;36(5):371-384. doi:10.3109/0142159X.2014.889290.
Fraser KL, Ayres P, Sweller J. Cognitive Load Theory for the Design of Medical Simulations. Simul Healthc. 2015;10(5):295-307. doi:10.1097/SIH.0000000000000097.
Leppink J, van den Heuvel A. The evolution of cognitive load theory and its application to medical education. Perspect Med Educ. 2015;4(3):119-127. doi:10.1007/s40037-015-0192-x.
Sweller J. Cognitive load theory, learning difficulty, and instructional design. Learn Instr. 1994;4(4):295-312.
Kolb DA. Experiential Learning: Experience as the Source of Learning and Development. Englewood Cliffs, NJ: Prentice-Hall; 1984.
Ng IKS, Tham SZL, Loh CWN, Teo DB. When I say ... Socratic questioning. Med Educ. 2024;58(9):1029-1031. doi:10.1111/medu.15397.
Schulz KF, Altman DG, Moher D; CONSORT Group. CONSORT 2010 statement: updated guidelines for reporting parallel group randomised trials. BMJ. 2010;340:c332. doi:10.1136/bmj.c332.
Hoffmann TC, Glasziou PP, Boutron I, Milne R, Perera R, Moher D, et al. Better reporting of interventions: Template for Intervention Description and Replication (TIDieR) checklist and guide. BMJ. 2014;348:g1687. doi:10.1136/bmj.g1687.
Indian Council of Medical Research. National Ethical Guidelines for Biomedical and Health Research Involving Human Participants. New Delhi: ICMR; 2017.
World Medical Association. World Medical Association Declaration of Helsinki: Ethical Principles for Medical Research Involving Human Participants. JAMA. 2025;333(1):71-74. doi:10.1001/jama.2024.21972.
Polit DF, Beck CT. The content validity index: are you sure you know what is being reported? Critique and recommendations. Res Nurs Health. 2006;29(5):489-497. doi:10.1002/nur.20147.
Hingorjo MR, Jaleel F. Analysis of one-best MCQs: the difficulty index, discrimination index and distractor efficiency. J Pak Med Assoc. 2012;62(2):142-147.
Kuder GF, Richardson MW. The theory of the estimation of test reliability. Psychometrika. 1937;2:151-160.
Tavakol M, Dennick R. Making sense of Cronbach’s alpha. Int J Med Educ. 2011;2:53-55. doi:10.5116/ijme.4dfb.8dfd.
Norman G. Likert scales, levels of measurement and the ‘laws’ of statistics. Adv Health Sci Educ Theory Pract. 2010;15(5):625-632. doi:10.1007/s10459-010-9222-y.
Bowen DJ, Kreuter M, Spring B, Cofta-Woerpel L, Linnan L, Weiner D, et al. How we design feasibility studies. Am J Prev Med. 2009;36(5):452-457. doi:10.1016/j.amepre.2009.02.002.
Vickers AJ, Altman DG. Statistics notes: analysing controlled trials with baseline and follow up measurements. BMJ. 2001;323(7321):1123-1124. doi:10.1136/bmj.323.7321.1123.
Lakens D. Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs. Front Psychol. 2013;4:863. doi:10.3389/fpsyg.2013.00863.
Hew KF, Lo CK. Flipped classroom improves student learning in health professions education: a meta-analysis. BMC Med Educ. 2018;18(1):38. doi:10.1186/s12909-018-1144-z.
