See one, simulate many, do one, teach one
Bibliographic record
Abstract
PURPOSE OF REVIEW: To review the cardiac surgical simulation experience with a focus on data supporting its use. RECENT FINDINGS: Simulators have been used to improve trainee performance across multiple surgical domains. Few cardiac surgery residency programs have incorporated the use of simulation individually and Boot Camp programs in the United States and Canada have also introduced surgical simulation early in cardiac surgical training. Simulation curricula have some common elements: component tasks, deliberate practice, progressive operative responsibility, and coaching by an experienced surgeon. Cardiac surgical simulators can range from inexpensive, low-fidelity models for the practice of isolated skills to high-fidelity, operating room-scenarios. Multiple small studies have consistently demonstrated that the use of simulation improves qualitative and quantitative performance measures as well as overall resident confidence in clinical settings. To our knowledge, no study has demonstrated that use of simulation has led to improved quantitative performance measures in the operating room or patient outcomes. The barriers to wider use of surgical simulators include perceived lack of time and resources, the need for sustained practice and the lack of high-quality data to demonstrate clinical benefit. SUMMARY: Incorporation of cardiac surgery simulation has been slow in most residency programs. There is consistent data demonstrating that simulation improves resident performance measures of simulation-based tasks but whether this will lead to improved patient outcomes remains an open question.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".