‘Live Die Repeat’ simulation for medical students
Bibliographic record
Abstract
The ‘Live.Die.Repeat’ (LDR) format for simulation-based education (SBE) involves repetition of scenario segments until adequate learner performance is achieved and emphasises repetitive practice over prolonged postscenario reflective debriefing.1 We incorporated the LDR format into our medical student simulations and suggest that it can be a useful element in a programmatic simulation curriculum, with appropriate preparation for learners and faculty. Background Simulation-based education (SBE) has been widely adopted as a learning method for health professional education and may also be enhanced by the integration of educational games - ‘an instructional method requiring the learner to participate in a competitive activity with preset rules’.2 In their ‘Live.Die.Repeat’ (LDR) study, Sunga et al designed a simulation scenario that incorporated gameplay to teach the management of emergent pulmonary conditions to postgraduate emergency medicine trainees.1 The design was based on recursive objective-based gameplay—‘a serious-game scheme in which participants are allowed infinite lives so that they can achieve predetermined criteria for progression through multiple levels of increasing difficulty’.1 The LDR format has parallels with rapid cycle deliberate practice (RCDP)3 simulation, a team-based simulation method, emphasising repetitive practice over reflective debriefing, with progressively more challenging rounds, frequent starts and stops and direct coaching. RCDP is well described for ‘algorithmic’ tasks like resuscitation, and the Sunga study was also undertaken with critical care postgraduate trainees in high acuity scenarios. We hypothesised that the format would also be effective for the lower acuity and less technical context of medical student education.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.055 | 0.011 |
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".