Utilization of a 3D Printed Simulation Training Model to Improve Microsurgical Training
Bibliographic record
Abstract
Background: Simulation is integral to the development and maintenance of micro- surgical skills. Several simulation models have been described ranging from bench- top to live animal models. High fidelity models are often burdened by cost and ethical issues limiting widespread implementation. This study aims to determine the feasibility of a microsurgical training platform using the Konjac noodle model. Methods: A prospective cohort study was conducted at our institution. A progressive microsurgical training curriculum was developed. A bespoke three-dimensional printed training platform was produced to enable residents to record training and assessment tasks. Microsurgical skills were blindly assessed before and after completing the training program using the University of Western Ontario Microsurgical Skills Assessment instrument. Results: Plastic surgery residents at various stages of training were recruited (n = 10). A significant improvement in vessel preparation from a pre-training median of 3 (IQR 2 -4) versus a post-training of 4 (IQR 3 -5, P = 0.0035) and suturing with a pre-training median of 3 (IQR 2 -4) versus a post-training of 4 (IQR 3 -5, P = 0.0047) domains of the University of Western Ontario Microsurgical Skills Assessment score was demonstrated after completion of the training program. There was a significant improvement in the global rating score (3 ± 1 versus 5 ± 1, P = 0.0045). Participants felt more confident performing a microsurgical anastomosis following the training program. Conclusion: The use of the Konjac noodle model and video-based assessment using a three-dimensional printed model is an effective teaching tool that improves resident's microsurgical skills.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".