Congenital Heart Surgery Skill Training Using Simulation Models: Not an Option but a Necessity
Bibliographic record
Abstract
Congenital heart surgery (CHS) is technically demanding, and its training is extremely complex and challenging. Training of the surgeon's technical skills has relied on a preceptorship format in which the trainees are gradually exposed to patients in the operating room under the close tutelage of senior staff surgeons. Training in the operating room is an inefficient process and the concept of a learning curve is no longer acceptable in terms of patient outcomes. The benefits of surgical simulation in training of congenital heart surgeons are well known and appreciated. However, adequate surgical simulation models and equipment for training have been scarce until the recent development of three-dimensionally (3D) printed models. Using comprehensive 3D printing and silicone-molding techniques, realistic simulation training models for most congenital heart surgical procedures have been produced. Newly developed silicone-molded models allow efficient CHS training in a stress-free environment with instantaneous feedback from the proctors and avoids risk to patients. The time has arrived when all congenital heart surgeons should consider surgical simulation training before progressing to real-life operating in a similar fashion to the aviation industry where all pilots are required to complete simulation training before flying a real aircraft. It is argued here that simulation training is not an option anymore but should be a mandatory component of CHS training.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".