Effectiveness of hybrid simulation training on medical student performance in whole-task consultation of cardiac patients: The ASSIMILATE EXCELLENCE randomized waitlist-controlled trial
Bibliographic record
Abstract
BACKGROUND: Assessment of comprehensive consultations in medicine, i.e. a complete history, physical examination, and differential diagnosis, is regarded as authentic tests of clinical competence; however, they have been shown to have low reliability and validity due to variability in the real patients used and subjective examiner grading. In the ASSIMILATE EXCELLENCE study, our aim was to assess the effect(s) of expert tuition with hybrid simulation using a simulated patient wearing a novel auscultation vest, i.e. a hybrid simulated patient, and repeated peer grading using scoring checklists on student learning, performance, and acumen in comprehensive consultations of patients with valvular heart disease. METHODS: ASSIMILATE EXCELLENCE was a randomized waitlist-controlled trial with blinded outcome assessment undertaken between February 2021 and November 2021. Students at the Royal College of Surgeons in Ireland in either the second or third year of the four-year graduate-entry medical degree programme were randomized to a hybrid simulation training or waitlist control group and undertook three consultation assessments of three different clinical presentations of valvular heart disease (cases: C1-C3) using hybrid simulation. Our primary outcome was the difference in total score between and within groups across time; a secondary outcome was any change in inter-rater reliability across time. Students self-reported their proficiency and confidence in comprehensive consultations using a pre- and post-study survey. RESULTS: Included were 68 students (age 27.6 ± 0.1 years; 74% women). Overall, total score was 39.6% (35.6, 44.9) in C1 and increased to 63.6% (56.7, 66.7) in C3 (P < .001). On intergroup analysis, a significant difference was observed between groups in C2 only (54.2 ± 7.1% vs. 45.6 ± 9.2%; P < .001), a finding that was mainly driven by a difference in physical examination score. On intragroup analysis, significant improvement in total score across time between cases was also observed. Intraclass correlation coefficients for each pair of assessors were excellent (0.885-0.996 [0.806, 0.998]) in all cases. Following participation, students' confidence in comprehensive consultation assessments improved, and they felt more prepared for their future careers. CONCLUSIONS: Hybrid simulation-based training improves competence and confidence in medical students undertaking comprehensive consultation assessment of cardiac patients. In addition, weighted scoring checklists improve grading consistency, learning through peer assessment, and feedback. Trial registration ClinicalTrials.gov Identifier: NCT05895799.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".