High Fidelity Microsurgical Simulation: The Thiel Model and Evaluation Instrument
Bibliographic record
Abstract
BACKGROUND: The Thiel embalmment method has recently been used in a number of medical simulation fields. The authors investigate the use of Thiel vessels as a high fidelity model for microvascular simulation and propose a new checklist-based evaluation instrument for microsurgical training. METHODS: Thirteen residents and 2 attending microsurgeons performed video recorded microvascular anastomoses on Thiel embalmed arteries that were evaluated using a new evaluation instrument (Microvascular Evaluation Scale) by 4 fellowship trained microsurgeons. The internal validity was assessed using the Cronbach coefficient. The external validity was verified using regression models. RESULTS: The reliability assessment revealed an excellent intra-class correlation of 0.89. When comparing scores obtained by participants from different levels of training, attending surgeons and senior residents (Post Graduate Year [PGY] 4-5) scored significantly better than junior residents (PGY 1-3). The difference between senior residents and attending surgeons was not significant. When considering microsurgical experience, the differences were significant between the advanced group and the minimal and moderate experience groups. The differences between minimal and moderate experience groups were not significant. Based on the data obtained, a score of 8 would translate into a level of microsurgical competence appropriate for clinical microsurgery. CONCLUSIONS: Thiel cadaveric vessels are a high fidelity model for microsurgical simulation. Excellent internal and external validity measures were obtained using the Microvascular Evaluation Scale (MVES).
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".