Integrating Virtual Patients Into Preclinical Education to Enhance Early Clinical Exposure and Skill Acquisition in Medical Students
Bibliographic record
Abstract
Tamer MM Abuamara,1,2 Wagih M Abd-Elhay,1 Hasan S AL-Ghamdi,3 Mohammad A Alghamdi,3 Razan Abed A Baloush,4 Dahlia Soleman A Mirdad,4 Ahmed Abdulwahab Bawahab,4 Mohammed Abd El Malik Hassan,5 Naji Al-bawah6 1Histology Department, Faculty of Medicine, Al-Azhar University, Cairo, Egypt; 2Center of Oral Diseases Studies, Faculty of Dentistry, Al-Ahliyya Amman University (AAU), Amman, Jordan; 3Internal Medicine Department, Division of Dermatology, Faculty of Medicine, Al-Baha University, Al-Baha, Kingdom of Saudi Arabia; 4Department of Basic Medical Sciences, Pathology Division, College of Medicine, University of Jeddah, Jeddah, Saudi Arabia; 5Pediatrics Department, Faculty of Medicine, Al-Azhar University, Cairo, Egypt; 6Faculty of Medicine, Sana’a University, Sana’a, YemenCorrespondence: Naji Al-bawah, Faculty of medicine, Sana’a University, Sana’a, 13078, Yemen, Email najihaa@sanaa.edu.yeBackground: Virtual patients (VPs) have traditionally been utilized in clinical education rather than in preclinical instruction. However, limited research has evaluated the effectiveness of VP-based sessions in enhancing early clinical exposure (ECE) during the preclinical years.Aim: To assess the impact of VP-based tutorials in supplementing the Clinical Skills module for second-year medical students.Methods: In this prospective study, the effectiveness of VP-based tutorials was compared to conventional lecture-based teaching within the Clinical Skills module. All second-year medical students enrolled during the 2022/2023 and 2023/2024 academic years were included. The 2022/2023 cohort (Group 1) received traditional lectures, while the 2023/2024 cohort (Group 2) participated in VP-based tutorials. Student performance was evaluated using pre- and post-module tests and formative Objective Structured Clinical Examination (OSCE) scores.Results: Baseline performance showed no significant difference in pre-module test scores between Group 1 (26.55 ± 22.45%) and Group 2 (27.13 ± 26.21%) (p = 0.681). Both groups demonstrated significant improvements in post-module scores (Group 1: 73.88 ± 15.11%; Group 2: 75.01 ± 10.09%, p< 0.0001 for both), with no significant difference between the two (p = 0.129). However, Group 2 achieved significantly higher OSCE scores compared to Group 1 (82.62 ± 11.03% vs 75.80 ± 14.38%, p< 0.0001).Conclusion: This study highlights the value of incorporating virtual patients into preclinical education. VP-based tutorials significantly enhance clinical skill development and facilitate early clinical exposure, offering a promising adjunct to traditional teaching methods in preclinical curricula.Keywords: virtual patients, medical education, early clinical exposure, preclinical medical education, clinical skills
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".