The National Pediatric Surgery Simulation Program in France: A tool to develop resident training in pediatric surgery
Bibliographic record
Abstract
BACKGROUND/PURPOSE: To implement resident curriculum in France based on theoretical teaching and bed side training, the national council known as the "Collège Hospitalier et Universitaire de Chirurgie Pédiatrique" examined the relevance and feasibility of systematically introducing simulation program in the pediatric surgery resident training. MATERIAL AND METHODS: A national simulation training program was developed and took place in a 2-day session organized in 7 simulation centers in France. The program included technical (laparoscopic/suturing technique on low-fidelity models) and nontechnical (6 scenarios for standardized consultation, and a team work scenario based on errors prevention in the operative room) skills. Evaluation of the program (Likert scale from 1 (bad) to 5 (excellent) and notation on 20 points) concerned trainees and trainers. RESULTS: 40 residents (95% of all pediatric surgery French residents) attended with a ratio of trainees/trainer of ½. The training objectives earned a score of 4.46/5. The pedagogical value of the seminar scored 4.7/5, teaching quality 17.95/20, and the overall seminar score was 17.35/20. CONCLUSION: This program, unique nationally, was assessed very favorably by the participating residents and by the involved trainers. To our knowledge, it represents the first mandatory national simulation training program included within a surgical training model. LEVEL OF EVIDENCE: Level IV.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".