Evaluating the Impact of Medical Student Inclusion Into Hands-On Surgical Simulation in Congenital Heart Surgery
Bibliographic record
Abstract
OBJECTIVE: Over the last decade medical students' interest in pursuing surgery as a career has declined. This is more apparent in high-specialized specialities such as congenital heart surgery (CHS). Early hands-on simulation has shown to have a positive impact on medical students' interest in pursuing surgery, however, its incorporation into medical school curricula is lacking. This study aimed to evaluate the impact of incorporating medical students as surgical assistants during the Hands On Surgical Training course in CHS. METHODS: Local preclinical medical students were invited to participate as surgical assistants during the 5th annual Hands On Surgical Training course in CHS. Among those who responded to the invitation, students were randomly selected and allocated to assist a congenital heart surgeon. All selected students attended an assistants' session prior to the course to familiarize themselves with assisting and to practice basic surgical skills. At the end of both courses students completed a questionnaire based on Likert 5-point scale to evaluate the courses' usefulness. RESULTS: Fifteen medical students completed the questionnaires. All reported a beginner level of understanding of congenital heart disease. All students were highly satisfied with using 3D-printed models to help their understanding of congenital heart disease (4.80 ± 0.41) and agreed that the sessions improved their assisting skills (4.93 ± 0.26). All expressed a desire to attend similar sessions in the future and agreed that surgical simulation inclusion into medical school curricula would enhance learning (5.00 ± 0.00). Interest in pursuing a career in CHS increased from 33% (5) to 87% (13) by the end of the course. CONCLUSIONS: Integration of preclinical medical students into surgical simulation increases interest in pursuing highly specialised surgical specialities such as CHS. Early exposure and the incorporation of such simulation programs into medical school curricula will likely improve surgical skill acquisition and may enable students to be better informed when selecting future career choices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".