Educational Role for an Advanced Suturing Task in the Pediatric Laparoscopic Surgery Simulator
Bibliographic record
Abstract
BACKGROUND: Laparoscopic models are recognized as important training tools. Lower fidelity systems are used mainly for simpler tasks; an advanced suturing task may allow for additional training of experts. The purpose of this study was to explore the educational role of an advanced suturing task using motion analysis and establish the task's construct validity. METHODS: The pediatric laparoscopic surgery (PLS) simulator was customized with motion-tracking hardware and software. Participants were stratified by expertise, then performed an advanced task involving intracorporeal suturing in a vertical plane, with the suture passing superiorly to inferiorly. Traditional PLS scores were calculated, and motion was analyzed in the four degrees of freedom available in laparoscopic surgery (Pitch, Yaw, Roll, and Surge). Data were compared to historic results for a standard suturing task. RESULTS: Sixty participants were recruited (8 novices, 13 intermediates, and 39 experts). Analysis of motion in all degrees of freedom allowed discrimination between participants based on expertise level. Compared with the standard task, PLS scores for the advanced task were significantly lower for intermediates and experts, and the number of extreme motion events was significantly higher, indicating that advanced task is more challenging. In addition, only 76.3% of experts, 76.9% of intermediates, and 37.5% of novices were able to successfully complete the advanced task. CONCLUSIONS: Performance of an advanced intracorporeal suturing task allowed discrimination of expertise level. The task's increased complexity may help hone laparoscopic technical skills, particularly among advanced performers, and even allow discrimination of psychomotor expertise within the traditional cohort of experts.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".