Comparison of Adult and Pediatric Surgeons: Insight into Simulation-Based Tools That May Improve Expertise Among Experts
Bibliographic record
Abstract
BACKGROUND: Laparoscopic models are increasingly recognized as important tools in surgical training. The purpose of this study was to compare pediatric and adult laparoscopic surgical skills, and gain insight into the upskilling in both groups. MATERIALS AND METHODS: Adult- and pediatric-sized laparoscopic simulators were fitted with custom-built motion tracking hardware and software. Participants were recruited at the Education Booth of the 2012 combined SAGES/IPEG meeting. They each performed 1 adult and 1 pediatric intracorporeal suturing task. Velocity, acceleration, and range were studied in all degrees of freedom available during laparoscopic surgery (pitch, yaw, roll, and surge). Participants were stratified by expertise based on the traditional metrics of self-reported caseloads. RESULTS: A total of 57 participants (15 novices, 7 intermediates, and 35 experts) were recruited. Experts had significantly higher extreme events in three of the four degrees of freedom when using the pediatric simulator than when using the adult simulator. Few significant differences were seen when comparing novice and intermediate performances on the adult versus pediatric simulator. Linear regression showed no difference between adult and pediatric experts tested on the adult or pediatric simulator. CONCLUSIONS: Experts were more challenged with the pediatric than with the adult suturing task. No difference was noted for overall averaged performance metrics comparing adult and pediatric experts suturing in adult versus pediatric simulators. As a participant's level of expertise improves, a model progressing from larger to smaller domains in the performance of defined laparoscopic tasks may, by virtue of its greater challenge, encourage psychomotor development.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".